{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T06:21:21Z","timestamp":1771482081965,"version":"3.50.1"},"reference-count":165,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T00:00:00Z","timestamp":1767830400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T00:00:00Z","timestamp":1769040000000},"content-version":"vor","delay-in-days":14,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["2021.05763.BD"],"award-info":[{"award-number":["2021.05763.BD"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Portuguese Recovery and Resilience Plan","award":["C645008882-00000055"],"award-info":[{"award-number":["C645008882-00000055"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"DOI":"10.1007\/s10462-025-11475-5","type":"journal-article","created":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T15:33:00Z","timestamp":1767886380000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A survey on group fairness in federated learning: challenges, taxonomy of solutions and directions for future research"],"prefix":"10.1007","volume":"59","author":[{"given":"Teresa","family":"Salazar","sequence":"first","affiliation":[]},{"given":"Helder","family":"Araujo","sequence":"additional","affiliation":[]},{"given":"Alberto","family":"Cano","sequence":"additional","affiliation":[]},{"given":"Pedro Henriques","family":"Abreu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,8]]},"reference":[{"key":"11475_CR1","doi-asserted-by":"crossref","unstructured":"Abay A, Chuba E, Zhou Y, Baracaldo N, Ludwig H (2021) Addressing unique fairness obstacles within federated learning. In: Proceedings of the workshop on reframing diversity in AI: representation, inclusion and power","DOI":"10.1007\/978-3-030-96896-0_8"},{"key":"11475_CR2","unstructured":"Abay A, Zhou Y, Baracaldo N, Rajamoni S, Chuba E, Ludwig H (2020) Mitigating bias in federated learning. arXiv preprint arXiv:2012.02447"},{"key":"11475_CR3","doi-asserted-by":"crossref","unstructured":"Abu-Elyounes D (2020) Contextual fairness: a legal and policy analysis of algorithmic fairness. U. Ill. JL Tech. & Pol\u2019y, 1","DOI":"10.2139\/ssrn.3478296"},{"key":"11475_CR4","first-page":"191","volume":"104","author":"A Act","year":"1996","unstructured":"Act A (1996) Health insurance portability and accountability act of 1996. Public Law 104:191","journal-title":"Public Law"},{"key":"11475_CR5","doi-asserted-by":"crossref","unstructured":"Agrawal N, Sirohi AK, Kumar S et al (2024) No prejudice! fair federated graph neural networks for personalized recommendation. In: Proceedings of the AAAI conference on artificial intelligence, vol 38, pp 10775\u201310783","DOI":"10.1609\/aaai.v38i10.28950"},{"key":"11475_CR6","doi-asserted-by":"crossref","unstructured":"Amiri S, Belloum A, Nalisnick E, Klous S, Gommans L (2022) On the impact of non-IID data on the performance and fairness of differentially private federated learning. In: 52nd annual IEEE\/IFIP international conference on dependable systems and networks workshops, pp 52\u201358","DOI":"10.1109\/DSN-W54100.2022.00018"},{"key":"11475_CR7","unstructured":"Angwin J, Larson J, Mattu S, Kirchner L (2016) Machine bias: there\u2019s software used across the country to predict future criminals and it\u2019s biased against black. In: ProPublica"},{"key":"11475_CR8","unstructured":"Annapareddy N, Preston J, Fox J (2023) Fairness and privacy in federated learning and their implications in healthcare. arXiv preprint arXiv:2308.07805"},{"key":"11475_CR9","unstructured":"Artificial\u00a0Intelligence ECH-LEG (2019) Ethics guidelines for trustworthy AI. Accessed: 01 March 2025. https:\/\/ec.europa.eu\/newsroom\/dae\/document.cfm?doc_id=60419"},{"key":"11475_CR10","unstructured":"Asuncion A, Newman DJ (2017) Adult data set. In: UCI machine learning repository"},{"key":"11475_CR11","unstructured":"Awasthi P, Kleindessner M, Morgenstern JH (2020) Equalized odds postprocessing under imperfect group information. In: international conference on artificial intelligence and statistics, pp 1770\u20131780"},{"issue":"5","key":"11475_CR12","first-page":"311","volume":"9","author":"D Ayres-de-Campos","year":"2000","unstructured":"Ayres-de-Campos D, Bernardes J, Garrido A, Marques-de-Sa J, Pereira-Leite L (2000) Sisporto 2.0: a program for automated analysis of cardiotocograms. J Maternal-Fetal Med 9(5):311\u2013318","journal-title":"J Maternal-Fetal Med"},{"key":"11475_CR13","doi-asserted-by":"crossref","unstructured":"Badar M, Sikdar S, Nejdl W, Fisichella M (2024) Fairtrade: achieving pareto-optimal trade-offs between balanced accuracy and fairness in federated learning. In: Proceedings of the AAAI conference on artificial intelligence, vol 38, pp 10962\u201310970","DOI":"10.1609\/aaai.v38i10.28971"},{"issue":"8","key":"11475_CR14","doi-asserted-by":"crossref","first-page":"1018","DOI":"10.1016\/S2214-109X(20)30285-0","volume":"8","author":"P Baqui","year":"2020","unstructured":"Baqui P, Bica I, Marra V, Ercole A, Der Schaar M (2020) Ethnic and regional variations in hospital mortality from COVID-19 in brazil: a cross-sectional observational study. Lancet Glob Health 8(8):1018\u20131026","journal-title":"Lancet Glob Health"},{"key":"11475_CR15","unstructured":"Benarba N, Bouchenak S (2023) Empirical analysis of bias in federated learning. In: Conf\u00e9rence Francophone D\u2019informatique en Parall\u00e9lisme, Architecture et Syst\u00e8me"},{"issue":"1","key":"11475_CR16","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1177\/0049124118782533","volume":"50","author":"R Berk","year":"2021","unstructured":"Berk R, Heidari H, Jabbari S, Kearns M, Roth A (2021) Fairness in criminal justice risk assessments: the state of the art. Sociol Methods Res 50(1):3\u201344","journal-title":"Sociol Methods Res"},{"key":"11475_CR17","doi-asserted-by":"crossref","unstructured":"Carey AN, Du W, Wu X (2022) Robust personalized federated learning under demographic fairness heterogeneity. In: IEEE international conference on big data, pp 1425\u20131434","DOI":"10.1109\/BigData55660.2022.10020554"},{"issue":"7","key":"11475_CR18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3616865","volume":"56","author":"S Caton","year":"2024","unstructured":"Caton S, Haas C (2024) Fairness in machine learning: a survey. ACM Comput Surv 56(7):1\u201338","journal-title":"ACM Comput Surv"},{"key":"11475_CR19","unstructured":"Chang H, Shokri R (2023) Bias propagation in federated learning. In: The eleventh international conference on learning representations"},{"key":"11475_CR20","unstructured":"Chaudhury BR, Murhekar A, Yuan Z, Li B, Mehta R, Procaccia AD (2024) Fair federated learning via the proportional veto core. In: Forty-first international conference on machine learning"},{"issue":"17","key":"11475_CR21","doi-asserted-by":"crossref","first-page":"2024789118","DOI":"10.1073\/pnas.2024789118","volume":"118","author":"M Chen","year":"2021","unstructured":"Chen M, Shlezinger N, Poor HV, Eldar YC, Cui S (2021) Communication-efficient federated learning. Proc Natl Acad Sci 118(17):2024789118","journal-title":"Proc Natl Acad Sci"},{"issue":"2","key":"11475_CR22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3606017","volume":"56","author":"H Chen","year":"2023","unstructured":"Chen H, Zhu T, Zhang T, Zhou W, Yu PS (2023) Privacy and fairness in federated learning: on the perspective of tradeoff. ACM Comput Surv 56(2):1\u201337","journal-title":"ACM Comput Surv"},{"key":"11475_CR23","doi-asserted-by":"crossref","first-page":"890","DOI":"10.1016\/j.future.2024.06.040","volume":"160","author":"C Chen","year":"2024","unstructured":"Chen C, Zhou Z, Tang P, He L, Su S (2024) Enforcing group fairness in privacy-preserving federated learning. Futur Gener Comput Syst 160:890\u2013900","journal-title":"Futur Gener Comput Syst"},{"key":"11475_CR24","doi-asserted-by":"crossref","first-page":"130698","DOI":"10.1109\/ACCESS.2021.3114099","volume":"9","author":"A Chhabra","year":"2021","unstructured":"Chhabra A, Masalkovait\u0117 K, Mohapatra P (2021) An overview of fairness in clustering. IEEE Access 9:130698\u2013130720","journal-title":"IEEE Access"},{"key":"11475_CR25","unstructured":"Cho YJ, Wang J, Joshi G (2022) Towards understanding biased client selection in federated learning. In: International conference on artificial intelligence and statistics, pp 10351\u201310375"},{"issue":"2","key":"11475_CR26","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1089\/big.2016.0047","volume":"5","author":"A Chouldechova","year":"2017","unstructured":"Chouldechova A (2017) Fair prediction with disparate impact: a study of bias in recidivism prediction instruments. Big Data 5(2):153\u2013163","journal-title":"Big Data"},{"key":"11475_CR27","unstructured":"Chu L, Wang L, Dong Y, Pei J, Zhou Z, Zhang Y (2021) FedFair: training fair models in cross-silo federated learning. arXiv preprint arXiv:2109.05662"},{"key":"11475_CR28","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1097\/01.MLR.0000076048.11549.71","volume":"41","author":"SB Cohen","year":"2003","unstructured":"Cohen SB (2003) Design strategies and innovations in the medical expenditure panel survey. Med Care 41:5","journal-title":"Med Care"},{"key":"11475_CR29","unstructured":"Congress US (2019) Algorithmic Accountability Act of 2019. https:\/\/www.congress.gov\/bill\/116th-congress\/house-bill\/2231. H.R.2231, 116th Congress. Accessed 01 March 2025"},{"issue":"1","key":"11475_CR30","first-page":"14730","volume":"24","author":"S Corbett-Davies","year":"2023","unstructured":"Corbett-Davies S, Gaebler JD, Nilforoshan H, Shroff R, Goel S (2023) The measure and mismeasure of fairness. The J Mach Learn Res 24(1):14730\u201314846","journal-title":"The J Mach Learn Res"},{"key":"11475_CR31","unstructured":"Crenshaw K (1989) Demarginalizing the intersection of race and sex: a black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics. University of Chicago Legal Forum 1989"},{"issue":"6","key":"11475_CR32","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1007\/s10462-025-11179-w","volume":"58","author":"A Cruz","year":"2025","unstructured":"Cruz A, Salazar T, Carvalho M, Ma\u00e7\u00e3s C, Machado P, Abreu PH (2025) Guidelines for designing visualization tools for group fairness analysis in binary classification. Artif Intell Rev 58(6):182","journal-title":"Artif Intell Rev"},{"key":"11475_CR33","first-page":"26091","volume":"34","author":"S Cui","year":"2021","unstructured":"Cui S, Pan W, Liang J, Zhang C, Wang F (2021) Addressing algorithmic disparity and performance inconsistency in federated learning. Adv Neural Inf Process Syst 34:26091\u201326102","journal-title":"Adv Neural Inf Process Syst"},{"key":"11475_CR34","unstructured":"Czerniak J (2009) Acute inflammations. UCI Machine Learning Repository"},{"issue":"6","key":"11475_CR35","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1109\/MSP.2012.2211477","volume":"29","author":"L Deng","year":"2012","unstructured":"Deng L (2012) The MNIST database of handwritten digit images for machine learning research. IEEE Signal Process Mag 29(6):141\u2013142","journal-title":"IEEE Signal Process Mag"},{"key":"11475_CR36","unstructured":"Dheeru D, Karra\u00a0Taniskidou E (2017) KDD census income data set. In: UCI machine learning repository"},{"key":"11475_CR37","first-page":"6478","volume":"34","author":"F Ding","year":"2021","unstructured":"Ding F, Hardt M, Miller J, Schmidt L (2021) Retiring adult: new datasets for fair machine learning. Adv Neural Inf Process Syst 34:6478\u20136490","journal-title":"Adv Neural Inf Process Syst"},{"issue":"4","key":"11475_CR38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3631455","volume":"7","author":"Y Djebrouni","year":"2024","unstructured":"Djebrouni Y, Benarba N, Touat O, De Rosa P, Bouchenak S, Bonifati A, Felber P, Marangozova V, Schiavoni V (2024) Bias mitigation in federated learning for edge computing. The proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies 7(4):1\u201335","journal-title":"The proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies"},{"issue":"12","key":"11475_CR39","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1097\/PAS.0000000000000749","volume":"40","author":"MA Duggan","year":"2016","unstructured":"Duggan MA, Anderson WF, Altekruse S, Penberthy L, Sherman ME (2016) The surveillance, epidemiology, and end results (seer) program and pathology: toward strengthening the critical relationship. Am J Surg Pathol 40(12):94\u2013102","journal-title":"Am J Surg Pathol"},{"key":"11475_CR40","doi-asserted-by":"crossref","unstructured":"Du W, Xu D, Wu X, Tong H (2021) Fairness-aware agnostic federated learning. In: Proceedings of the SIAM international conference on data mining, pp 181\u2013189","DOI":"10.1137\/1.9781611976700.21"},{"key":"11475_CR41","doi-asserted-by":"crossref","unstructured":"Dwork C (2006) Differential privacy. In: International colloquium on automata, languages, and programming, pp 1\u201312","DOI":"10.1007\/11787006_1"},{"key":"11475_CR42","doi-asserted-by":"crossref","unstructured":"Dwork C, Hardt M, Pitassi T, Reingold O, Zemel R (2012) Fairness through awareness. In: Proceedings of the 3rd innovations in theoretical computer science conference, pp 214\u2013226","DOI":"10.1145\/2090236.2090255"},{"key":"11475_CR43","unstructured":"Economic\u00a0Co-operation O (OECD) D (2019) OECD principles on artificial intelligence. https:\/\/www.oecd.org\/going-digital\/ai\/principles\/. Accessed: 2025-03-01"},{"key":"11475_CR44","doi-asserted-by":"crossref","first-page":"22359","DOI":"10.1109\/ACCESS.2022.3151670","volume":"10","author":"A El Ouadrhiri","year":"2022","unstructured":"El Ouadrhiri A, Abdelhadi A (2022) Differential privacy for deep and federated learning: a survey. IEEE Access 10:22359\u201322380","journal-title":"IEEE Access"},{"key":"11475_CR45","unstructured":"European Parliament, Council of the European Union: Regulation (EU) 2024\/1689 of the European Parliament and of the Council. https:\/\/data.europa.eu\/eli\/reg\/2024\/1689\/oj. Accessed 07 March 2025"},{"key":"11475_CR46","doi-asserted-by":"crossref","unstructured":"Ezzeldin YH, Yan S, He C, Ferrara E, Avestimehr AS (2023) Fairfed: enabling group fairness in federated learning. In: Proceedings of the AAAI conference on artificial intelligence, vol 37, pp 7494\u20137502","DOI":"10.1609\/aaai.v37i6.25911"},{"key":"11475_CR47","unstructured":"Fan Z, Fang H, Wang X, Zhou Z, Pei J, Friedlander M, Zhang Y (2024) Fair and efficient contribution valuation for vertical federated learning. In: The twelfth international conference on learning representations"},{"key":"11475_CR48","doi-asserted-by":"crossref","unstructured":"Fehrman E, Muhammad AK, Mirkes EM, Egan V, Gorban AN (2017) The five factor model of personality and evaluation of drug consumption risk. In: Data science: innovative developments in data analysis and clustering, pp 231\u2013242","DOI":"10.1007\/978-3-319-55723-6_18"},{"key":"11475_CR49","doi-asserted-by":"crossref","unstructured":"Fernandes M, Silva C, Arrais J, Cardoso A, Ribeiro B (2021) Decay momentum for improving federated learning. In: European symposium on artificial neural networks","DOI":"10.14428\/esann\/2021.ES2021-106"},{"key":"11475_CR50","doi-asserted-by":"crossref","unstructured":"Friedler SA, Scheidegger C, Venkatasubramanian S, Choudhary S, Hamilton EP, Roth D (2019) A comparative study of fairness-enhancing interventions in machine learning. In: Proceedings of the conference on fairness, accountability, and transparency, pp 329\u2013338","DOI":"10.1145\/3287560.3287589"},{"issue":"4","key":"11475_CR51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2523813","volume":"46","author":"J Gama","year":"2014","unstructured":"Gama J, \u017dliobait\u0117 I, Bifet A, Pechenizkiy M, Bouchachia A (2014) A survey on concept drift adaptation. ACM Comput Surv 46(4):1\u201337","journal-title":"ACM Comput Surv"},{"key":"11475_CR52","doi-asserted-by":"crossref","first-page":"47849","DOI":"10.52202\/079017-1516","volume":"37","author":"J Gao","year":"2024","unstructured":"Gao J, Wang Z, Zhao X, Yao X, Wei X (2024) Does egalitarian fairness lead to instability? The fairness bounds in stable federated learning under altruistic behaviors. Adv Neural Inf Process Syst 37:47849\u201347875","journal-title":"Adv Neural Inf Process Syst"},{"key":"11475_CR53","unstructured":"Gao J, Huang C, Tang M, Tan SH, Yao X, Wei X (2023) EFFL: egalitarian fairness in federated learning for mitigating Matthew effect. arXiv preprint arXiv:2309.16338"},{"key":"#cr-split#-11475_CR54.1","unstructured":"GDPR GDPR (2016) General data protection regulation. Regulation"},{"key":"#cr-split#-11475_CR54.2","unstructured":"(EU) 2016\/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95\/46\/EC"},{"key":"11475_CR55","volume-title":"Heritage health prize","author":"A Goldbloom","year":"2011","unstructured":"Goldbloom A, Hamner B (2011) Heritage health prize. Kaggle, San Francisco"},{"key":"11475_CR56","volume":"122","author":"X Gu","year":"2022","unstructured":"Gu X, Tianqing Z, Li J, Zhang T, Ren W, Choo K-KR (2022) Privacy, accuracy, and model fairness trade-offs in federated learning. Comput Secur 122:102907","journal-title":"Comput Secur"},{"key":"11475_CR57","doi-asserted-by":"crossref","unstructured":"Hamman F, Dutta S (2024) Demystifying local & global fairness trade-offs in federated learning using partial information decomposition. In: International conference on learning representations","DOI":"10.1109\/ISIT57864.2024.10619698"},{"key":"11475_CR58","volume":"287","author":"M Han","year":"2024","unstructured":"Han M, Zhu T, Zhou W (2024) Fair federated learning with opposite GAN. Knowl-Based Syst 287:111420","journal-title":"Knowl-Based Syst"},{"key":"11475_CR59","unstructured":"Hardt M, Price E, Srebro N (2016) Equality of opportunity in supervised learning. Adv Neural Inf Process Syst 29"},{"issue":"4","key":"11475_CR60","first-page":"1","volume":"5","author":"FM Harper","year":"2015","unstructured":"Harper FM, Konstan JA (2015) The movielens datasets: history and context. ACM Trans Inter Intell Syst 5(4):1\u201319","journal-title":"ACM Trans Inter Intell Syst"},{"key":"11475_CR65","doi-asserted-by":"crossref","unstructured":"Hu S, Wu ZS, Smith V (2024) Fair federated learning via bounded group loss. In: 2024 IEEE conference on secure and trustworthy machine learning, pp 140\u2013160","DOI":"10.1109\/SaTML59370.2024.00015"},{"issue":"8","key":"11475_CR61","first-page":"1","volume":"57","author":"K Hu","year":"2024","unstructured":"Hu K, Gong S, Zhang Q, Seng C, Xia M, Jiang S (2024) An overview of implementing security and privacy in federated learning. Artif Intell Rev 57(8):1\u201366","journal-title":"Artif Intell Rev"},{"key":"11475_CR64","unstructured":"Huang C, Huang J, Liu X (2022) Cross-silo federated learning: challenges and opportunities. arXiv preprint arXiv:2206.12949"},{"issue":"4","key":"11475_CR62","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MCOM.005.2300467","volume":"62","author":"C Huang","year":"2023","unstructured":"Huang C, Tang M, Ma Q, Huang J, Liu X (2023) Promoting collaboration in cross-silo federated learning: challenges and opportunities. IEEE Commun Mag 62(4):82\u201388","journal-title":"IEEE Commun Mag"},{"issue":"12","key":"11475_CR63","doi-asserted-by":"crossref","first-page":"9387","DOI":"10.1109\/TPAMI.2024.3418862","volume":"46","author":"W Huang","year":"2024","unstructured":"Huang W, Ye M, Shi Z, Wan G, Li H, Du B, Yang Q (2024) Federated learning for generalization, robustness, fairness: a survey and benchmark. IEEE Trans Pattern Anal Mach Intell 46(12):9387\u20139406","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"11475_CR66","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2016.35","volume":"3","author":"AE Johnson","year":"2016","unstructured":"Johnson AE, Pollard TJ, Shen L, Lehman L-WH, Feng M, Ghassemi M, Moody B, Szolovits P, Anthony Celi L, Mark RG (2016) MIMIC-III, a freely accessible critical care database. Sci Data 3(1):1\u20139","journal-title":"Sci Data"},{"issue":"1","key":"11475_CR67","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41597-022-01899-x","volume":"10","author":"AE Johnson","year":"2023","unstructured":"Johnson AE, Bulgarelli L, Shen L, Gayles A, Shammout A, Horng S, Pollard TJ, Hao S, Moody B, Gow B et al (2023) MIMIC-IV, a freely accessible electronic health record dataset. Sci Data 10(1):1","journal-title":"Sci Data"},{"issue":"1","key":"11475_CR68","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10115-011-0463-8","volume":"33","author":"F Kamiran","year":"2012","unstructured":"Kamiran F, Calders T (2012) Data preprocessing techniques for classification without discrimination. Knowl Inf Syst 33(1):1\u201333","journal-title":"Knowl Inf Syst"},{"key":"11475_CR69","doi-asserted-by":"crossref","unstructured":"Kamishima T, Akaho S, Asoh H, Sakuma J (2012) Fairness-aware classifier with prejudice remover regularizer. In: Machine learning and knowledge discovery in databases: European conference, pp 35\u201350","DOI":"10.1007\/978-3-642-33486-3_3"},{"key":"11475_CR70","doi-asserted-by":"crossref","unstructured":"Kanaparthy S, Padala M, Damle S, Gujar S (2022) Fair federated learning for heterogeneous data. In: Proceedings of the 5th Joint international conference on data science & management of data, pp 298\u2013299","DOI":"10.1145\/3493700.3493750"},{"issue":"3","key":"11475_CR71","doi-asserted-by":"crossref","first-page":"191","DOI":"10.7326\/0003-4819-122-3-199502010-00007","volume":"122","author":"W Knaus","year":"1995","unstructured":"Knaus W, Harrell F, Lynn J, Goldman L, Phillips R, Connors A Jr, Dawson N, Fulkerson W, Califf R, Desbiens N, Layde P, Oye R, Bellamy P, Hakim R, Wagner D (1995) The support prognostic model: objective estimates of survival for seriously ill hospitalized adults. Ann Intern Med 122(3):191\u2013203","journal-title":"Ann Intern Med"},{"key":"11475_CR72","doi-asserted-by":"crossref","first-page":"1467222","DOI":"10.3389\/fdata.2024.1467222","volume":"7","author":"D Kowald","year":"2024","unstructured":"Kowald D, Scher S, Pammer-Schindler V, M\u00fcllner P, Waxnegger K, Demelius L, Fessl A, Toller M, Mendoza Estrada IG, \u0160imi\u0107 I et al (2024) Establishing and evaluating trustworthy AI: overview and research challenges. Front Big Data 7:1467222","journal-title":"Front Big Data"},{"key":"11475_CR73","unstructured":"Krizhevsky A, Hinton G (2009) Learning multiple layers of features from tiny images. Master\u2019s thesis, Department of Computer Science, University of Toronto"},{"key":"11475_CR74","doi-asserted-by":"crossref","unstructured":"Kyllo A, Mashhadi A (2023) Inflorescence: a framework for evaluating fairness with clustered federated learning. In: Adjunct proceedings of the ACM international joint conference on pervasive and ubiquitous computing & the ACM international symposium on wearable computing, pp 374\u2013380","DOI":"10.1145\/3594739.3610678"},{"key":"11475_CR75","unstructured":"Laan P (2000) The 2001 census in the netherlands. In: The census of population"},{"issue":"3","key":"11475_CR76","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1002\/widm.1452","volume":"12","author":"T Le Quy","year":"2022","unstructured":"Le Quy T, Roy A, Iosifidis V, Zhang W, Ntoutsi E (2022) A survey on datasets for fairness-aware machine learning. Wiley Interdiscip Rev Data Mining and Knowl Discov 12(3):1452","journal-title":"Wiley Interdiscip Rev Data Mining and Knowl Discov"},{"key":"11475_CR77","doi-asserted-by":"crossref","unstructured":"Lee J, Kim S, Kim S, Park J, Sohn K (2019) Context-aware emotion recognition networks. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 10143\u201310152","DOI":"10.1109\/ICCV.2019.01024"},{"issue":"3","key":"11475_CR78","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/MSP.2020.2975749","volume":"37","author":"T Li","year":"2020","unstructured":"Li T, Sahu AK, Talwalkar A, Smith V (2020) Federated learning: challenges, methods, and future directions. IEEE Signal Process Mag 37(3):50\u201360","journal-title":"IEEE Signal Process Mag"},{"key":"11475_CR82","unstructured":"Li T, Sanjabi M, Beirami A, Smith V (2020) Fair resource allocation in federated learning. In: International conference on learning representations"},{"issue":"9","key":"11475_CR79","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3555803","volume":"55","author":"B Li","year":"2023","unstructured":"Li B, Qi P, Liu B, Di S, Liu J, Pei J, Yi J, Zhou B (2023) Trustworthy AI: from principles to practices. ACM Comput Surv 55(9):1\u201346","journal-title":"ACM Comput Surv"},{"key":"11475_CR80","unstructured":"Liang PP, Liu T, Ziyin L, Allen NB, Auerbach RP, Brent D, Salakhutdinov R, Morency L-P (2020) Think locally, act globally: federated learning with local and global representations. arXiv preprint arXiv:2001.01523"},{"key":"11475_CR81","doi-asserted-by":"crossref","first-page":"46547","DOI":"10.2196\/46547","volume":"25","author":"X Liang","year":"2023","unstructured":"Liang X, Zhao J, Chen Y, Bandara E, Shetty S (2023) Architectural design of a blockchain-enabled, federated learning platform for algorithmic fairness in predictive health care: design science study. J Med Internet Res 25:46547","journal-title":"J Med Internet Res"},{"key":"11475_CR84","doi-asserted-by":"crossref","unstructured":"Liu Z, Luo P, Wang X, Tang X (2015) Deep learning face attributes in the wild. In: Proceedings of international conference on computer vision, pp 3730\u20133738","DOI":"10.1109\/ICCV.2015.425"},{"key":"11475_CR83","unstructured":"Liu C, Fan Z, Zhou Z, Shi Y, Pei J, Chu L, Zhang Y (2021) Achieving model fairness in vertical federated learning. arXiv preprint arXiv:2109.08344"},{"key":"11475_CR85","doi-asserted-by":"crossref","unstructured":"Lucaj L, Van Der Smagt P, Benbouzid D (2023) Ai regulation is (not) all you need. In: Proceedings of the 2023 ACM conference on fairness, accountability, and transparency, pp 1267\u20131279","DOI":"10.1145\/3593013.3594079"},{"key":"11475_CR87","doi-asserted-by":"crossref","unstructured":"Lyu L, Yu H, Yang Q (2020) Threats to federated learning: a survey. arXiv preprint arXiv:2003.02133","DOI":"10.1007\/978-3-030-63076-8_1"},{"issue":"7","key":"11475_CR86","doi-asserted-by":"crossref","first-page":"8726","DOI":"10.1109\/TNNLS.2022.3216981","volume":"35","author":"L Lyu","year":"2022","unstructured":"Lyu L, Yu H, Ma X, Chen C, Sun L, Zhao J, Yang Q, Philip SY (2022) Privacy and robustness in federated learning: attacks and defenses. IEEE Trans neural Netw Learn Syst 35(7):8726\u20138746","journal-title":"IEEE Trans neural Netw Learn Syst"},{"key":"11475_CR88","unstructured":"Mashhadi A, Kyllo A, Parizi RM (2022) Fairness in federated learning for spatial-temporal applications. arXiv preprint arXiv:2201.06598"},{"key":"11475_CR89","unstructured":"Matthey L, Higgins I, Hassabis D, Lerchner A (2017) dSprites: disentanglement testing Sprites dataset. Available from DeepMind"},{"key":"11475_CR90","unstructured":"McMahan HB, Moore E, Ramage D, Hampson S, Arcass BA (2017) Communication-efficient learning of deep networks from decentralized data. In: AAAI fall symposium, Google, Inc"},{"key":"11475_CR91","unstructured":"Meerza SIA, Liu L, Zhang J, Liu J (2024) Glocalfair: jointly improving global and local group fairness in federated learning. arXiv preprint arXiv:2401.03562"},{"issue":"6","key":"11475_CR92","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3457607","volume":"54","author":"N Mehrabi","year":"2021","unstructured":"Mehrabi N, Morstatter F, Saxena N, Lerman K, Galstyan A (2021) A survey on bias and fairness in machine learning. ACM Comput Surv 54(6):1\u201335","journal-title":"ACM Comput Surv"},{"key":"11475_CR93","unstructured":"Mehrabi N, Lichy C, McKay J, He C, Campbell W (2022) Towards multi-objective statistically fair federated learning. arXiv preprint arXiv:2201.09917"},{"key":"11475_CR94","doi-asserted-by":"crossref","unstructured":"Mishler A, Kennedy EH, Chouldechova A (2021) Fairness in risk assessment instruments: post-processing to achieve counterfactual equalized odds. In: Proceedings of the ACM conference on fairness, accountability, and transparency, pp 386\u2013400","DOI":"10.1145\/3442188.3445902"},{"key":"11475_CR95","unstructured":"Mitchell S, Potash E, Barocas S, D\u2019Amour A, Lum K (2018) Prediction-based decisions and fairness: a catalogue of choices, assumptions, and definitions. arXiv preprint arXiv:1811.07867"},{"key":"11475_CR96","doi-asserted-by":"crossref","unstructured":"Mohamad\u00a0Dunda GW, Song S (2024) Fairness-aware federated minimax optimization with convergence guarantee. In: IEEE conference on artificial intelligence, pp 563\u2013568","DOI":"10.1109\/CAI59869.2024.00111"},{"key":"11475_CR97","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.dss.2014.03.001","volume":"62","author":"S Moro","year":"2014","unstructured":"Moro S, Cortez P, Rita P (2014) A data-driven approach to predict the success of bank telemarketing. Decis Support Syst 62:22\u201331","journal-title":"Decis Support Syst"},{"key":"11475_CR98","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1016\/j.future.2020.10.007","volume":"115","author":"V Mothukuri","year":"2021","unstructured":"Mothukuri V, Parizi RM, Pouriyeh S, Huang Y, Dehghantanha A, Srivastava G (2021) A survey on security and privacy of federated learning. Futur Gener Comput Syst 115:619\u2013640","journal-title":"Futur Gener Comput Syst"},{"key":"11475_CR99","doi-asserted-by":"crossref","unstructured":"Nafea M, Shin E, Yener A (2022) Proportional fair clustered federated learning. In: 2022 IEEE international symposium on information theory","DOI":"10.1109\/ISIT50566.2022.9834545"},{"key":"11475_CR100","unstructured":"Nakamura K (2024) Ethics in artificial intelligence: addressing bias, fairness, and accountability in machine learning. Adv Comput Sci 7(1)"},{"key":"11475_CR101","doi-asserted-by":"crossref","unstructured":"Ni J, Li J, McAuley J (2019) Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pp 188\u2013197","DOI":"10.18653\/v1\/D19-1018"},{"key":"11475_CR102","doi-asserted-by":"crossref","unstructured":"Padala M, Damle S, Gujar S (2021) Federated learning meets fairness and differential privacy. In: Neural information processing: 28th international conference, pp 692\u2013699","DOI":"10.1007\/978-3-030-92310-5_80"},{"key":"11475_CR103","doi-asserted-by":"crossref","unstructured":"Pan C, Xu J, Yu Y, Yang Z, Wu Q, Wang C, Chen L, Yang Y (2024) Towards fair graph federated learning via incentive mechanisms. In: Proceedings of the AAAI conference on artificial intelligence, vol 38, pp 14499\u201314507","DOI":"10.1609\/aaai.v38i13.29365"},{"key":"11475_CR104","doi-asserted-by":"crossref","unstructured":"Papadaki A, Martinez N, Bertran M, Sapiro G, Rodrigues M (2022) Minimax demographic group fairness in federated learning. In: Proceedings of the ACM conference on fairness, accountability, and transparency, pp 142\u2013159","DOI":"10.1145\/3531146.3533081"},{"key":"11475_CR105","unstructured":"Pentyala S, Neophytou N, Nascimento A, De\u00a0Cock M, Farnadi G (2022) PRIVFAIRFL: privacy-preserving group fairness in federated learning. arXiv preprint arXiv:2205.11584"},{"issue":"3","key":"11475_CR106","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3494672","volume":"55","author":"D Pessach","year":"2022","unstructured":"Pessach D, Shmueli E (2022) A review on fairness in machine learning. ACM Comput Surv 55(3):1\u201344","journal-title":"ACM Comput Surv"},{"issue":"1","key":"11475_CR107","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2018.178","volume":"5","author":"TJ Pollard","year":"2018","unstructured":"Pollard TJ, Johnson AE, Raffa JD, Celi LA, Mark RG, Badawi O (2018) The eiCU collaborative research database, a freely available multi-center database for critical care research. Sci Data 5(1):1\u201313","journal-title":"Sci Data"},{"key":"11475_CR108","doi-asserted-by":"crossref","unstructured":"Poulain R, Bin\u00a0Tarek MF, Beheshti R (2023) Improving fairness in AI models on electronic health records: the case for federated learning methods. In: Proceedings of the ACM conference on fairness, accountability, and transparency, pp 1599\u20131608","DOI":"10.1145\/3593013.3594102"},{"key":"11475_CR109","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.aiopen.2023.08.003","volume":"4","author":"H Qiu","year":"2023","unstructured":"Qiu H, Feng R, Hu R, Yang X, Lin S, Tao Q, Yang Y (2023) Learning fair representations via an adversarial framework. AI Open 4:91\u201397","journal-title":"AI Open"},{"key":"11475_CR110","volume":"105","author":"TH Rafi","year":"2024","unstructured":"Rafi TH, Noor FA, Hussain T, Chae D-K (2024) Fairness and privacy preserving in federated learning: a survey. Inf Fusion 105:102198","journal-title":"Inf Fusion"},{"issue":"3","key":"11475_CR111","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1016\/S0377-2217(01)00264-8","volume":"141","author":"M Redmond","year":"2002","unstructured":"Redmond M, Baveja A (2002) A data-driven software tool for enabling cooperative information sharing among police departments. Eur J Oper Res 141(3):660\u2013678","journal-title":"Eur J Oper Res"},{"key":"11475_CR112","unstructured":"Rodr\u00edguez-G\u00e1lvez B, Granqvist F, Dalen R, Seigel M (2021) Enforcing fairness in private federated learning via the modified method of differential multipliers. In: NeurIPS workshop"},{"key":"11475_CR113","unstructured":"Roffo G (2006) Ads-16 computational advertising dataset. In: Kaggle"},{"key":"11475_CR114","unstructured":"Roh Y, Lee K, Whang SE, Suh C (2021) Fairbatch: batch selection for model fairness. In: International conference on learning representations"},{"issue":"1","key":"11475_CR115","first-page":"35","volume":"15","author":"S Saha","year":"2021","unstructured":"Saha S, Ahmad T (2021) Federated transfer learning: concept and applications. Intell Artif 15(1):35\u201344","journal-title":"Intell Artif"},{"issue":"7","key":"11475_CR116","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1007\/s10462-024-10766-7","volume":"57","author":"S Saha","year":"2024","unstructured":"Saha S, Hota A, Chattopadhyay AK, Nag A, Nandi S (2024) A multifaceted survey on privacy preservation of federated learning: progress, challenges, and opportunities. Artif Intell Rev 57(7):184","journal-title":"Artif Intell Rev"},{"key":"11475_CR117","doi-asserted-by":"crossref","first-page":"81370","DOI":"10.1109\/ACCESS.2021.3084121","volume":"9","author":"T Salazar","year":"2021","unstructured":"Salazar T, Santos MS, Ara\u00fajo H, Abreu PH (2021) Fawos: fairness-aware oversampling algorithm based on distributions of sensitive attributes. IEEE Access 9:81370\u201381379","journal-title":"IEEE Access"},{"key":"11475_CR118","doi-asserted-by":"crossref","unstructured":"Salazar T, Fernandes M, Ara\u00fajo H, Abreu PH (2023) Fair-fate: fair federated learning with momentum. In: International conference on computational science, pp 524\u2013538","DOI":"10.1007\/978-3-031-35995-8_37"},{"key":"11475_CR119","unstructured":"Salazar T, Gama J, Ara\u00fajo H, Abreu PH (2024) Unveiling group-specific distributed concept drift: a fairness imperative in federated learning. arXiv preprint arXiv:2402.07586"},{"key":"11475_CR120","doi-asserted-by":"crossref","unstructured":"Selialia K, Chandio Y, Anwar FM (2024) Mitigating group bias in federated learning for heterogeneous devices. In: Proceedings of the ACM conference on fairness, accountability, and transparency, pp 1043\u20131054","DOI":"10.1145\/3630106.3658954"},{"issue":"Suppl 2","key":"11475_CR121","doi-asserted-by":"crossref","first-page":"1773","DOI":"10.1007\/s10462-023-10563-8","volume":"56","author":"Y Shanmugarasa","year":"2023","unstructured":"Shanmugarasa Y, Paik H-Y, Kanhere SS, Zhu L (2023) A systematic review of federated learning from clients\u2019 perspective: challenges and solutions. Artif Intell Rev 56(Suppl 2):1773\u20131827","journal-title":"Artif Intell Rev"},{"issue":"9","key":"11475_CR122","doi-asserted-by":"crossref","first-page":"11922","DOI":"10.1109\/TNNLS.2023.3263594","volume":"35","author":"Y Shi","year":"2024","unstructured":"Shi Y, Yu H, Leung C (2024) Towards fairness-aware federated learning. IEEE Trans Neural Netw Learn Syst 35(9):11922\u201311938","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"3","key":"11475_CR123","first-page":"102","volume":"32","author":"M Sobek","year":"1999","unstructured":"Sobek M, Ruggles S (1999) The IPUMS project: an update. Hist Methods A J Quant Inter Hist 32(3):102\u2013110","journal-title":"Hist Methods A J Quant Inter Hist"},{"key":"11475_CR126","doi-asserted-by":"crossref","unstructured":"Su C, Yu G, Wang J, Li H, Li Q, Yu H (2024) Multi-dimensional fair federated learning. In: Proceedings of the AAAI conference on artificial intelligence, vol. 38, pp 15083\u201315090","DOI":"10.1609\/aaai.v38i13.29430"},{"key":"11475_CR124","doi-asserted-by":"crossref","unstructured":"Sun K, Zhang X, Lin X, Li G, Wang J, Li J (2023) Toward the tradeoffs between privacy, fairness and utility in federated learning. In: International symposium on emerging information security and applications, pp 118\u2013132","DOI":"10.1007\/978-981-99-9614-8_8"},{"key":"11475_CR125","unstructured":"Sun Z, Zhang Z, Xu Z, Joshi G, Sharma P, Wei E (2025) Debiasing federated learning with correlated client participation. In: The thirteenth international conference on learning representations"},{"key":"11475_CR127","doi-asserted-by":"crossref","unstructured":"Torres RLS, Ranasinghe DC, Shi Q, Sample AP (2013) Sensor enabled wearable RFID technology for mitigating the risk of falls near beds. In: IEEE International conference on RFID, pp 191\u2013198","DOI":"10.1109\/RFID.2013.6548154"},{"key":"11475_CR128","doi-asserted-by":"crossref","unstructured":"Touat O, Bouchenak S (2023) Towards robust and bias-free federated learning. In: Proceedings of the 3rd workshop on machine learning and systems, pp 49\u201355","DOI":"10.1145\/3578356.3592576"},{"key":"11475_CR129","doi-asserted-by":"crossref","unstructured":"Truex S, Baracaldo N, Anwar A, Steinke T, Ludwig H, Zhang R, Zhou Y (2019) A hybrid approach to privacy-preserving federated learning. In: Proceedings of the 12th ACM workshop on artificial intelligence and security, pp 1\u201311","DOI":"10.1145\/3338501.3357370"},{"key":"11475_CR130","doi-asserted-by":"crossref","unstructured":"Ude B, Odeyomi OT, Roy K, Yuan X (2023) A survey on bias mitigation in federated learning. In: IEEE symposium series on computational intelligence, pp 1170\u20131175","DOI":"10.1109\/SSCI52147.2023.10372031"},{"issue":"4","key":"11475_CR131","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1007\/s10462-023-10663-5","volume":"57","author":"B Vass\u00f8y","year":"2024","unstructured":"Vass\u00f8y B, Langseth H (2024) Consumer-side fairness in recommender systems: a systematic survey of methods and evaluation. Artif Intell Rev 57(4):101","journal-title":"Artif Intell Rev"},{"key":"11475_CR132","doi-asserted-by":"crossref","unstructured":"Vavoulas G, Chatzaki C, Malliotakis T, Pediaditis M, Tsiknakis M (2016) The mobiact dataset: recognition of activities of daily living using smartphones. In: international conference on information and communication technologies for ageing well and E-health, vol 2, SciTePress, pp 143\u2013151","DOI":"10.5220\/0005792401430151"},{"key":"11475_CR133","doi-asserted-by":"crossref","unstructured":"Verma S, Rubin J (2018) Fairness definitions explained. In: Proceedings of the international workshop on software fairness, pp 1\u20137","DOI":"10.1145\/3194770.3194776"},{"key":"11475_CR134","doi-asserted-by":"crossref","first-page":"80903","DOI":"10.1109\/ACCESS.2023.3295412","volume":"11","author":"S Vucinich","year":"2023","unstructured":"Vucinich S, Zhu Q (2023) The current state and challenges of fairness in federated learning. IEEE Access 11:80903\u201380914","journal-title":"IEEE Access"},{"issue":"3","key":"11475_CR135","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1093\/jamia\/ocx079","volume":"25","author":"J Walonoski","year":"2018","unstructured":"Walonoski J, Kramer M, Nichols J, Quina A, Moesel C, Hall D, Duffett C, Dube K, Gallagher T, McLachlan S (2018) Synthea: an approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record. J Am Med Inform Assoc 25(3):230\u2013238","journal-title":"J Am Med Inform Assoc"},{"key":"11475_CR138","doi-asserted-by":"crossref","unstructured":"Wang A, Ramaswamy VV, Russakovsky O (2022) Towards intersectionality in machine learning: Including more identities, handling underrepresentation, and performing evaluation. In: Proceedings of the ACM conference on fairness, accountability, and transparency, pp 336\u2013349","DOI":"10.1145\/3531146.3533101"},{"key":"11475_CR137","unstructured":"Wang G, Payani A, Lee M, Kompella RR (2024) Mitigating group bias in federated learning: beyond local fairness. Trans Mach Learn Res"},{"key":"11475_CR136","first-page":"1","volume":"8","author":"T Wang","year":"2024","unstructured":"Wang T, Zhang K, Cai J, Gong Y, Choo K-KR, Guo Y (2024) Analyzing the impact of personalization on fairness in federated learning for healthcare. J Healthc Inf Res 8:1\u201325","journal-title":"J Healthc Inf Res"},{"key":"11475_CR139","doi-asserted-by":"crossref","unstructured":"Wang Z, Wang Z, Lyu L, Peng Z, Yang Z, Wen C, Yu R, Wang C, Fan X (2024) Fedsac: dynamic submodel allocation for collaborative fairness in federated learning. In: Proceedings of the 30th ACM SIGKDD conference on knowledge discovery and data mining, pp 3299\u20133310","DOI":"10.1145\/3637528.3671748"},{"key":"11475_CR140","unstructured":"Wei K, Li J, Ma C, Ding M, Wei S, Wu F, Chen G, Ranbaduge T (2022) Vertical federated learning: challenges, methodologies and experiments. arXiv preprint arXiv:2202.04309"},{"key":"11475_CR141","unstructured":"Wightman LF (1998) Lsac national longitudinal bar passage study. In: LSAC research report series"},{"key":"11475_CR142","unstructured":"Xiao H, Rasul K, Vollgraf R (2017) Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747"},{"key":"11475_CR143","doi-asserted-by":"crossref","unstructured":"Xu R, Chen Z, Zuo W, Yan J, Lin L (2018) Deep cocktail network: multi-source unsupervised domain adaptation with category shift. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3964\u20133973","DOI":"10.1109\/CVPR.2018.00417"},{"key":"11475_CR144","doi-asserted-by":"crossref","unstructured":"Yang Y, Jiang B (2022) Towards group fairness via semi-centralized adversarial training in federated learning. In: 23rd IEEE international conference on mobile data management, pp 482\u2013487","DOI":"10.1109\/MDM55031.2022.00103"},{"key":"11475_CR145","unstructured":"Yang Y, Payani A, Naghizadeh P (2024) Enhancing group fairness in federated learning through personalization. arXiv preprint arXiv:2407.19331"},{"key":"11475_CR146","doi-asserted-by":"crossref","unstructured":"Yang C, Wang Q, Xu M, Chen Z, Bian K, Liu Y, Liu X (2021) Characterizing impacts of heterogeneity in federated learning upon large-scale smartphone data. In: Proceedings of the web conference 2021, pp 935\u2013946","DOI":"10.1145\/3442381.3449851"},{"issue":"3","key":"11475_CR147","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3625558","volume":"56","author":"M Ye","year":"2023","unstructured":"Ye M, Fang X, Du B, Yuen PC, Tao D (2023) Heterogeneous federated learning: state-of-the-art and research challenges. ACM Comput Surv 56(3):1\u201344","journal-title":"ACM Comput Surv"},{"issue":"2","key":"11475_CR148","doi-asserted-by":"crossref","first-page":"2473","DOI":"10.1016\/j.eswa.2007.12.020","volume":"36","author":"I-C Yeh","year":"2009","unstructured":"Yeh I-C, Lien C-H (2009) The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert Syst Appl 36(2):2473\u20132480","journal-title":"Expert Syst Appl"},{"issue":"6","key":"11475_CR149","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3460427","volume":"54","author":"X Yin","year":"2021","unstructured":"Yin X, Zhu Y, Hu J (2021) A comprehensive survey of privacy-preserving federated learning: a taxonomy, review, and future directions. ACM Comput Surv (CSUR) 54(6):1\u201336","journal-title":"ACM Comput Surv (CSUR)"},{"key":"11475_CR150","unstructured":"Yin Q, Huang J, Yao H, Zhang L (2024) Distribution-free fair federated learning with small samples. arXiv preprint arXiv:2402.16158"},{"key":"11475_CR151","doi-asserted-by":"crossref","unstructured":"Zafar MB, Valera I, Gomez\u00a0Rodriguez M, Gummadi KP (2017) Fairness beyond disparate treatment & disparate impact: learning classification without disparate mistreatment. In: Proceedings of the 26th international conference on World Wide Web, pp 1171\u20131180","DOI":"10.1145\/3038912.3052660"},{"key":"11475_CR152","unstructured":"Zeng Y, Chen H, Lee K (2021) Improving fairness via federated learning. arXiv preprint arXiv:2110.15545"},{"key":"11475_CR153","doi-asserted-by":"crossref","unstructured":"Zeng Y, Chen H, Lee K (2023) Federated learning with local fairness constraints. In: IEEE international symposium on information theory, pp 1937\u20131942","DOI":"10.1109\/ISIT54713.2023.10206590"},{"key":"11475_CR154","unstructured":"Zeng R, Zeng C, Wang X, Li B, Chu X (2021) A comprehensive survey of incentive mechanism for federated learning. arXiv preprint arXiv:2106.15406"},{"key":"11475_CR162","doi-asserted-by":"crossref","unstructured":"Zhang Z, Song Y, Qi H (2017) Age progression\/regression by conditional adversarial autoencoder. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5810\u20135818","DOI":"10.1109\/CVPR.2017.463"},{"key":"11475_CR155","doi-asserted-by":"crossref","unstructured":"Zhang BH, Lemoine B, Mitchell M (2018) Mitigating unwanted biases with adversarial learning. In: Proceedings of the AAAI\/ACM conference on AI, ethics, and society, pp 335\u2013340","DOI":"10.1145\/3278721.3278779"},{"key":"11475_CR156","doi-asserted-by":"crossref","unstructured":"Zhang DY, Kou Z, Wang D (2020) Fairfl: a fair federated learning approach to reducing demographic bias in privacy-sensitive classification models. In: 2020 IEEE international conference on big data, pp 1051\u20131060","DOI":"10.1109\/BigData50022.2020.9378043"},{"key":"11475_CR157","volume":"216","author":"C Zhang","year":"2021","unstructured":"Zhang C, Xie Y, Bai H, Yu B, Li W, Gao Y (2021) A survey on federated learning. Knowl-Based Syst 216:106775","journal-title":"Knowl-Based Syst"},{"key":"11475_CR160","unstructured":"Zhang F, Kuang K, Liu Y, Chen L, Wu C, Wu F, Lu J, Shao Y, Xiao J (2021) Unified group fairness on federated learning. arXiv preprint arXiv:2111.04986"},{"issue":"1","key":"11475_CR158","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1109\/IOTM.004.2100182","volume":"5","author":"T Zhang","year":"2022","unstructured":"Zhang T, Gao L, He C, Zhang M, Krishnamachari B, Avestimehr AS (2022) Federated learning for the internet of things: applications, challenges, and opportunities. IEEE Int Things Mag 5(1):24\u201329","journal-title":"IEEE Int Things Mag"},{"key":"11475_CR161","doi-asserted-by":"crossref","unstructured":"Zhang F, Shuai Z, Kuang K, Wu F, Zhuang Y, Xiao J (2024) Unified fair federated learning for digital healthcare. Patterns 5(1)","DOI":"10.1016\/j.patter.2023.100907"},{"issue":"1","key":"11475_CR159","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/s43681-023-00398-y","volume":"4","author":"D Zhang","year":"2024","unstructured":"Zhang D, Pan S, Hoang T, Xing Z, Staples M, Xu X, Yao L, Lu Q, Zhu L (2024) To be forgotten or to be fair: unveiling fairness implications of machine unlearning methods. AI Ethics 4(1):83\u201393","journal-title":"AI Ethics"},{"key":"11475_CR163","unstructured":"Zhao Y, Li M, Lai L, Suda N, Civin D, Chandra V (2018) Federated learning with non-IID data. arXiv preprint arXiv:1806.00582"},{"key":"11475_CR164","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.neucom.2021.07.098","volume":"465","author":"H Zhu","year":"2021","unstructured":"Zhu H, Xu J, Liu S, Jin Y (2021) Federated learning on non-IID data: a survey. Neurocomput 465:371\u2013390","journal-title":"Neurocomput"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-025-11475-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-025-11475-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-025-11475-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T05:49:39Z","timestamp":1771480179000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-025-11475-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,8]]},"references-count":165,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["11475"],"URL":"https:\/\/doi.org\/10.1007\/s10462-025-11475-5","relation":{},"ISSN":["1573-7462"],"issn-type":[{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,8]]},"assertion":[{"value":"4 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors report that there are no potential Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"81"}}