{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T09:58:51Z","timestamp":1774259931388,"version":"3.50.1"},"reference-count":48,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001459","name":"Government of Singapore Ministry of Education","doi-asserted-by":"publisher","award":["RG91\/22"],"award-info":[{"award-number":["RG91\/22"]}],"id":[{"id":"10.13039\/501100001459","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001475","name":"Nanyang Technological University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001475","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52404040"],"award-info":[{"award-number":["52404040"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Applied Soft Computing"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.asoc.2026.114982","type":"journal-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T07:50:14Z","timestamp":1773042614000},"page":"114982","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["FedUnit: A Robust and Fair Federated Learning Framework for Malicious Client Detection and Free-Rider Contribution Evaluation"],"prefix":"10.1016","volume":"195","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7701-6313","authenticated-orcid":false,"given":"Leiming","family":"Chen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4990-0646","authenticated-orcid":false,"given":"Hina","family":"Batool","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4371-0953","authenticated-orcid":false,"given":"Xingjie","family":"Zeng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3637-4939","authenticated-orcid":false,"given":"Dehai","family":"Zhao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0346-1824","authenticated-orcid":false,"given":"Yongsheng","family":"Du","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6624-9752","authenticated-orcid":false,"given":"Chee Wei","family":"Tan","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.asoc.2026.114982_bib0005","doi-asserted-by":"crossref","first-page":"1","DOI":"10.58496\/BJIoT\/2023\/001","article-title":"Federated learning in IoT: a survey on distributed decision making","volume":"2023","author":"Hameed","year":"2023","journal-title":"Babylon. J. Internet Things"},{"key":"10.1016\/j.asoc.2026.114982_bib0010","doi-asserted-by":"crossref","first-page":"76","DOI":"10.58496\/BJML\/2025\/006","article-title":"Privacy-preserving transfer learning for community detection in multiple networks: a review","volume":"2025","author":"Rosli","year":"2025","journal-title":"Babyl. J. Mach. Learn."},{"key":"10.1016\/j.asoc.2026.114982_bib0015","series-title":"Artificial Intelligence and Statistics","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2017"},{"key":"10.1016\/j.asoc.2026.114982_bib0020","doi-asserted-by":"crossref","first-page":"46","DOI":"10.70470\/SHIFRA\/2025\/002","article-title":"Federated learning in healthcare: a bibliometric analysis of privacy, security, and adversarial threats (2021\u20132024)","volume":"2025","author":"Almaiah","year":"2025","journal-title":"Shifra"},{"key":"10.1016\/j.asoc.2026.114982_bib0025","doi-asserted-by":"crossref","first-page":"13","DOI":"10.70470\/ESTIDAMAA\/2025\/002","article-title":"Federated learning for smart and sustainable forest fire detection in green internet of things","volume":"2025","author":"Ali","year":"2025","journal-title":"ESTIDAMAA"},{"issue":"3","key":"10.1016\/j.asoc.2026.114982_bib0030","article-title":"Attack detection in internet of medical things through ensemble machine learning models","volume":"8","author":"Sharma","year":"2025","journal-title":"Sec. Priv."},{"key":"10.1016\/j.asoc.2026.114982_bib0035","doi-asserted-by":"crossref","first-page":"26","DOI":"10.70470\/EDRAAK\/2024\/004","article-title":"Fortifying AI against cyber threats advancing resilient systems to combat adversarial attacks","volume":"2024","author":"Hussain","year":"2024","journal-title":"EDRAAK"},{"key":"10.1016\/j.asoc.2026.114982_bib0040","first-page":"77","article-title":"Artificial intelligence in malware and network intrusion detection: a comprehensive survey of techniques, datasets, challenges, and future directions","volume":"2025","author":"Moamin","year":"2025","journal-title":"Babyl. J. Artif. Intell."},{"key":"10.1016\/j.asoc.2026.114982_bib0045","doi-asserted-by":"crossref","first-page":"31","DOI":"10.58496\/BJIoT\/2023\/005","article-title":"Safeguarding connected health: leveraging trustworthy AI techniques to harden intrusion detection systems against data poisoning threats in iomt environments","volume":"2023","author":"Aljanabi","year":"2023","journal-title":"Babylon. J. Internet Things"},{"key":"10.1016\/j.asoc.2026.114982_bib0050","article-title":"Machine learning with adversaries: byzantine tolerant gradient descent","volume":"30","author":"Blanchard","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"2","key":"10.1016\/j.asoc.2026.114982_bib0055","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1109\/TAI.2024.3355362","article-title":"A credible and fair federated learning framework based on blockchain","volume":"6","author":"Chen","year":"2025","journal-title":"IEEE Trans. Artif. Intell."},{"key":"10.1016\/j.asoc.2026.114982_bib0060","series-title":"IEEE International Conference on Communications","first-page":"1","article-title":"FedSV: byzantine-robust federated learning via shapley value","author":"Otmani","year":"2024"},{"key":"10.1016\/j.asoc.2026.114982_bib0065","doi-asserted-by":"crossref","first-page":"9714","DOI":"10.1109\/TIFS.2024.3477912","article-title":"CareFL: contribution guided byzantine-robust federated learning","volume":"19","author":"Dong","year":"2024","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"10.1016\/j.asoc.2026.114982_bib0070","series-title":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"2096","article-title":"ShapleyFL: robust federated learning based on shapley value","author":"Sun","year":"2023"},{"issue":"1","key":"10.1016\/j.asoc.2026.114982_bib0075","doi-asserted-by":"crossref","first-page":"1","DOI":"10.31577\/cai_2024_1_1","article-title":"FedDRL: trustworthy federated learning model fusion method based on staged reinforcement learning","volume":"43","author":"Chen","year":"2024","journal-title":"Comput. Inform."},{"issue":"1","key":"10.1016\/j.asoc.2026.114982_bib0080","doi-asserted-by":"crossref","first-page":"96","DOI":"10.3390\/e26010096","article-title":"FedTKD: a trustworthy heterogeneous federated learning based on adaptive knowledge distillation","volume":"26","author":"Chen","year":"2024","journal-title":"Entropy"},{"key":"10.1016\/j.asoc.2026.114982_bib0085","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2025.113610","article-title":"Personalized federated learning with fairness, robustness, and collaboration incentives","author":"Sabah","year":"2025","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2026.114982_bib0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2024.112405","article-title":"Privacy preserving verifiable federated learning scheme using blockchain and homomorphic encryption","volume":"167","author":"Mahato","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2026.114982_bib0095","article-title":"A distributed learning framework with blockchain and privacy-preserving for IoV","author":"Li","year":"2025","journal-title":"Appl. Soft Comput."},{"issue":"3","key":"10.1016\/j.asoc.2026.114982_bib0100","first-page":"1","article-title":"A meta-learning framework for tuning parameters of protection mechanisms in trustworthy federated learning","volume":"15","author":"Zhang","year":"2024","journal-title":"ACM Trans. Intell. Syst. Technol."},{"issue":"1","key":"10.1016\/j.asoc.2026.114982_bib0105","doi-asserted-by":"crossref","first-page":"1374","DOI":"10.1109\/JIOT.2023.3288936","article-title":"PASS: a parameter audit-based secure and fair federated learning scheme against free-rider attack","volume":"11","author":"Wang","year":"2023","journal-title":"IEEE Internet Things J."},{"issue":"5","key":"10.1016\/j.asoc.2026.114982_bib0110","doi-asserted-by":"crossref","first-page":"4677","DOI":"10.1109\/JIOT.2022.3218755","article-title":"Adaptive upgrade of client resources for improving the quality of federated learning model","volume":"10","author":"AbdulRahman","year":"2022","journal-title":"IEEE Internet Things J."},{"issue":"15","key":"10.1016\/j.asoc.2026.114982_bib0115","doi-asserted-by":"crossref","first-page":"25648","DOI":"10.1109\/JIOT.2024.3379363","article-title":"TrustBCFL: mitigating data bias in IoT through blockchain-enabled federated learning","volume":"11","author":"Zhou","year":"2024","journal-title":"IEEE Internet Things J."},{"issue":"2","key":"10.1016\/j.asoc.2026.114982_bib0120","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1109\/LNET.2024.3363620","article-title":"Greedy shapley client selection for communication-efficient federated learning","volume":"6","author":"Singhal","year":"2024","journal-title":"IEEE Netw. Lett."},{"key":"10.1016\/j.asoc.2026.114982_bib0125","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"12396","article-title":"Contribution-aware federated learning for smart healthcare","author":"Liu","year":"2022"},{"key":"10.1016\/j.asoc.2026.114982_bib0130","series-title":"2023 IEEE International Conference on Multimedia and Expo (ICME)","first-page":"672","article-title":"SWATM: contribution-aware adaptive federated learning framework based on augmented shapley values","author":"Yang","year":"2023"},{"key":"10.1016\/j.asoc.2026.114982_bib0135","doi-asserted-by":"crossref","DOI":"10.1016\/j.jpdc.2024.104994","article-title":"Towards value-sensitive and poisoning-proof model aggregation for federated learning on heterogeneous data","volume":"196","author":"Zeng","year":"2025","journal-title":"J. Parallel Distrib. Comput."},{"key":"10.1016\/j.asoc.2026.114982_bib0140","series-title":"2023 IEEE 3rd International Conference on Digital Twins and Parallel Intelligence (DTPI)","first-page":"1","article-title":"Shapley-value-based contribution evaluation in federated learning: a survey","author":"Zhu","year":"2023"},{"key":"10.1016\/j.asoc.2026.114982_bib0145","series-title":"2022 IEEE 38th International Conference on Data Engineering (ICDE)","first-page":"2440","article-title":"Improving fairness for data valuation in horizontal federated learning","author":"Fan","year":"2022"},{"key":"10.1016\/j.asoc.2026.114982_bib0150","author":"Fan"},{"key":"10.1016\/j.asoc.2026.114982_bib0155","series-title":"IEEE INFOCOM 2024-IEEE Conference on Computer Communications","first-page":"621","article-title":"FairFed: improving fairness and efficiency of contribution evaluation in federated learning via cooperative shapley value","author":"Liu","year":"2024"},{"key":"10.1016\/j.asoc.2026.114982_bib0160","series-title":"2019 IEEE International Conference on Big Data (Big Data)","first-page":"2577","article-title":"Profit allocation for federated learning","author":"Song","year":"2019"},{"key":"10.1016\/j.asoc.2026.114982_bib0165","author":"Yang"},{"issue":"4","key":"10.1016\/j.asoc.2026.114982_bib0170","first-page":"1","article-title":"GTG-shapley: efficient and accurate participant contribution evaluation in federated learning","volume":"13","author":"Liu","year":"2022","journal-title":"ACM Trans. Intell. Syst. Technol."},{"issue":"6","key":"10.1016\/j.asoc.2026.114982_bib0175","doi-asserted-by":"crossref","first-page":"1028","DOI":"10.1109\/TBDATA.2022.3198733","article-title":"WTDP-shapley: efficient and effective incentive mechanism in federated learning for intelligent safety inspection","volume":"10","author":"Yang","year":"2024","journal-title":"IEEE Trans. Big Data"},{"key":"10.1016\/j.asoc.2026.114982_bib0180","series-title":"2024 4th International Conference on Sustainable Expert Systems (ICSES)","first-page":"1204","article-title":"PIM-FFL: evaluating participation index metrics for fairness enhancement in federated learning","author":"Batool","year":"2024"},{"issue":"2","key":"10.1016\/j.asoc.2026.114982_bib0185","doi-asserted-by":"crossref","first-page":"1612","DOI":"10.1109\/TDSC.2024.3446864","article-title":"FairReward: towards fair reward distribution using equity theory in blockchain-based federated learning","volume":"22","author":"Chen","year":"2025","journal-title":"IEEE Trans. Dependable Secure Comput."},{"key":"10.1016\/j.asoc.2026.114982_bib0190","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.jpdc.2022.01.019","article-title":"FGFL: a blockchain-based fair incentive governor for federated learning","volume":"163","author":"Gao","year":"2022","journal-title":"J. Parallel Distrib. Comput."},{"issue":"6","key":"10.1016\/j.asoc.2026.114982_bib0195","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1109\/TBDATA.2022.3183614","article-title":"FedFAIM: a model performance-based fair incentive mechanism for federated learning","volume":"10","author":"Shi","year":"2024","journal-title":"IEEE Trans. Big Data"},{"key":"10.1016\/j.asoc.2026.114982_bib0200","doi-asserted-by":"crossref","first-page":"8140","DOI":"10.1109\/TIFS.2024.3433537","article-title":"FDFL: fair and discrepancy-aware incentive mechanism for federated learning","volume":"19","author":"Chen","year":"2024","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"10.1016\/j.asoc.2026.114982_bib0205","series-title":"2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS)","first-page":"762","article-title":"Ensuring fairness in federated learning services: innovative approaches to client selection, scheduling, and rewards","author":"Zhang","year":"2024"},{"key":"10.1016\/j.asoc.2026.114982_bib0210","author":"Li"},{"issue":"6","key":"10.1016\/j.asoc.2026.114982_bib0215","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1109\/JSAC.2019.2904348","article-title":"Adaptive federated learning in resource constrained edge computing systems","volume":"37","author":"Wang","year":"2019","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"10.1016\/j.asoc.2026.114982_bib0220","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"5751","article-title":"If you like shapley then you\u2019ll love the core","volume":"vol. 35","author":"Yan","year":"2021"},{"key":"10.1016\/j.asoc.2026.114982_bib0225","series-title":"International Conference on Machine Learning","first-page":"5650","article-title":"Byzantine-robust distributed learning: towards optimal statistical rates","author":"Yin","year":"2018"},{"key":"10.1016\/j.asoc.2026.114982_bib0230","doi-asserted-by":"crossref","first-page":"4574","DOI":"10.1109\/TIFS.2021.3108434","article-title":"Privacy-enhanced federated learning against poisoning adversaries","volume":"16","author":"Liu","year":"2021","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"10.1016\/j.asoc.2026.114982_bib0235","doi-asserted-by":"crossref","first-page":"1639","DOI":"10.1109\/TIFS.2022.3169918","article-title":"ShieldFL: mitigating model poisoning attacks in privacy-preserving federated learning","volume":"17","author":"Ma","year":"2022","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"10.1016\/j.asoc.2026.114982_bib0240","series-title":"2019 IEEE International Conference on Big Data (Big Data)","first-page":"2597","article-title":"Measure contribution of participants in federated learning","author":"Wang","year":"2019"}],"container-title":["Applied Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494626004308?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494626004308?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T09:01:51Z","timestamp":1774256511000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1568494626004308"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":48,"alternative-id":["S1568494626004308"],"URL":"https:\/\/doi.org\/10.1016\/j.asoc.2026.114982","relation":{},"ISSN":["1568-4946"],"issn-type":[{"value":"1568-4946","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"FedUnit: A Robust and Fair Federated Learning Framework for Malicious Client Detection and Free-Rider Contribution Evaluation","name":"articletitle","label":"Article Title"},{"value":"Applied Soft Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.asoc.2026.114982","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"114982"}}