{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T10:33:49Z","timestamp":1750070029146,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031333736"},{"type":"electronic","value":"9783031333743"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-33374-3_38","type":"book-chapter","created":{"date-parts":[[2023,5,26]],"date-time":"2023-05-26T10:02:30Z","timestamp":1685095350000},"page":"483-494","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["F3: Fair and\u00a0Federated Face Attribute Classification with\u00a0Heterogeneous Data"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8219-6868","authenticated-orcid":false,"given":"Samhita","family":"Kanaparthy","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8918-4739","authenticated-orcid":false,"given":"Manisha","family":"Padala","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1460-6102","authenticated-orcid":false,"given":"Sankarshan","family":"Damle","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4134-1154","authenticated-orcid":false,"given":"Ravi Kiran","family":"Sarvadevabhatla","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4634-7862","authenticated-orcid":false,"given":"Sujit","family":"Gujar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,27]]},"reference":[{"key":"38_CR1","unstructured":"Agarwal, A., Beygelzimer, A., Dudik, M., Langford, J., Wallach, H.: A reductions approach to fair classification. In: ICML, pp. 60\u201369 (2018)"},{"key":"38_CR2","unstructured":"Augenstein, S., Hard, A., Partridge, K., Mathews, R.: Jointly learning from decentralized (federated) and centralized data to mitigate distribution shift. In: NeurIPS 2021 Workshop on Distribution Shifts: Connecting Methods and Applications (2021)"},{"issue":"2","key":"38_CR3","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1089\/big.2016.0047","volume":"5","author":"A Chouldechova","year":"2017","unstructured":"Chouldechova, A.: Fair prediction with disparate impact: a study of bias in recidivism prediction instruments. Big Data 5(2), 153\u2013163 (2017)","journal-title":"Big Data"},{"key":"38_CR4","unstructured":"Ezzeldin, Y.H., Yan, S., He, C., Ferrara, E., Avestimehr, S.: FairFed: enabling group fairness in federated learning. In: NeurIPS Workshop on New Frontiers in Federated Learning (NFFL) (2021)"},{"key":"38_CR5","unstructured":"Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. In: NeurIPS, vol. 29, pp. 3315\u20133323 (2016)"},{"key":"38_CR6","unstructured":"Hu, S., Wu, Z.S., Smith, V.: Provably fair federated learning via bounded group loss. In: ICLR Workshop on Socially Responsible Machine Learning (2022)"},{"key":"38_CR7","doi-asserted-by":"crossref","unstructured":"Jung, S., Chun, S., Moon, T.: Learning fair classifiers with partially annotated group labels. In: CVPR, pp. 10348\u201310357 (2022)","DOI":"10.1109\/CVPR52688.2022.01010"},{"key":"38_CR8","unstructured":"Kanaparthy, S., Padala, M., Damle, S., Sarvadevabhatla, R.K., Gujar, S.: F3: fair and federated face attribute classification with heterogeneous data. arXiv preprint arXiv:2109.02351 (2021)"},{"key":"38_CR9","doi-asserted-by":"crossref","unstructured":"Karkkainen, K., Joo, J.: Fairface: face attribute dataset for balanced race, gender, and age for bias measurement and mitigation. In: WACV, pp. 1548\u20131558 (2021)","DOI":"10.1109\/WACV48630.2021.00159"},{"key":"38_CR10","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: CVPR, pp. 4401\u20134410 (2019)","DOI":"10.1109\/CVPR.2019.00453"},{"key":"38_CR11","unstructured":"Kone\u010dn\u1ef3, J., McMahan, H.B., Ramage, D., Richt\u00e1rik, P.: Federated optimization: distributed machine learning for on-device intelligence. arXiv preprint arXiv:1610.02527 (2016)"},{"key":"38_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1007\/978-3-030-58610-2_22","volume-title":"Computer Vision \u2013 ECCV 2020","author":"VS Lokhande","year":"2020","unstructured":"Lokhande, V.S., Akash, A.K., Ravi, S.N., Singh, V.: FairALM: augmented Lagrangian method for training fair models with little regret. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12357, pp. 365\u2013381. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58610-2_22"},{"key":"38_CR13","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: AISTATS (2017)"},{"key":"38_CR14","doi-asserted-by":"crossref","unstructured":"Padala, M., Damle, S., Gujar, S.: Federated learning meets fairness and differential privacy. In: ICONIP, pp. 692\u2013699 (2021)","DOI":"10.1007\/978-3-030-92310-5_80"},{"key":"38_CR15","doi-asserted-by":"crossref","unstructured":"Padala, M., Gujar, S.: FNNC: achieving fairness through neural networks. In: IJCAI, pp. 2277\u20132283 (2020)","DOI":"10.24963\/ijcai.2020\/315"},{"key":"38_CR16","doi-asserted-by":"crossref","unstructured":"Ruan, Y., Joe-Wong, C.: Fedsoft: soft clustered federated learning with proximal local updating. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 8124\u20138131 (2022)","DOI":"10.1609\/aaai.v36i7.20785"},{"issue":"1","key":"38_CR17","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1109\/TTS.2021.3111823","volume":"3","author":"P Terh\u00f6rst","year":"2021","unstructured":"Terh\u00f6rst, P., et al.: A comprehensive study on face recognition biases beyond demographics. IEEE Trans. Technol. Soc. 3(1), 16\u201330 (2021)","journal-title":"IEEE Trans. Technol. Soc."},{"key":"38_CR18","doi-asserted-by":"crossref","unstructured":"Torralba, A., Efros, A.A.: Unbiased look at dataset bias. In: CVPR, pp. 1521\u20131528 (2011)","DOI":"10.1109\/CVPR.2011.5995347"},{"key":"38_CR19","unstructured":"Zafar, M.B., Valera, I., Rogriguez, M.G., Gummadi, K.P.: Fairness constraints: mechanisms for fair classification. In: AISTATS, pp. 962\u2013970 (2017)"},{"key":"38_CR20","doi-asserted-by":"crossref","unstructured":"Zhang, B.H., Lemoine, B., Mitchell, M.: Mitigating unwanted biases with adversarial learning. In: AIES, pp. 335\u2013340 (2018)","DOI":"10.1145\/3278721.3278779"},{"key":"38_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, D.Y., Kou, Z., Wang, D.: FairFL: a fair federated learning approach to reducing demographic bias in privacy-sensitive classification models. In: IEEE Big Data, pp. 1051\u20131060 (2020)","DOI":"10.1109\/BigData50022.2020.9378043"},{"key":"38_CR22","doi-asserted-by":"crossref","unstructured":"Zhang, J., Wu, Y., Pan, R.: Incentive mechanism for horizontal federated learning based on reputation and reverse auction. In: WWW, pp. 947\u2013956 (2021)","DOI":"10.1145\/3442381.3449888"},{"key":"38_CR23","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Song, Y., Qi, H.: Age progression\/regression by conditional adversarial autoencoder. In: CVPR, pp. 5810\u20135818 (2017)","DOI":"10.1109\/CVPR.2017.463"},{"key":"38_CR24","unstructured":"Zhao, H., Gordon, G.: Inherent tradeoffs in learning fair representations. In: NeurIPS, vol. 32, pp. 15675\u201315685 (2019)"},{"issue":"8","key":"38_CR25","doi-asserted-by":"publisher","first-page":"2002","DOI":"10.1007\/s11263-020-01308-z","volume":"128","author":"X Zheng","year":"2020","unstructured":"Zheng, X., Guo, Y., Huang, H., Li, Y., He, R.: A survey of deep facial attribute analysis. Int. J. Comput. Vision 128(8), 2002\u20132034 (2020)","journal-title":"Int. J. Comput. Vision"}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-33374-3_38","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T20:05:13Z","timestamp":1710360313000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-33374-3_38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031333736","9783031333743"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-33374-3_38","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"27 May 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Osaka","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 May 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 May 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pakdd2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"813","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"143","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"18% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"10","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}