{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T04:37:55Z","timestamp":1773376675170,"version":"3.50.1"},"reference-count":21,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,6,25]],"date-time":"2023-06-25T00:00:00Z","timestamp":1687651200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,6,25]],"date-time":"2023-06-25T00:00:00Z","timestamp":1687651200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,25]]},"DOI":"10.1109\/isit54713.2023.10206590","type":"proceedings-article","created":{"date-parts":[[2023,8,22]],"date-time":"2023-08-22T13:31:15Z","timestamp":1692711075000},"page":"1937-1942","source":"Crossref","is-referenced-by-count":4,"title":["Federated Learning with Local Fairness Constraints"],"prefix":"10.1109","author":[{"given":"Yuchen","family":"Zeng","sequence":"first","affiliation":[{"name":"University of Wisconsin,Madison"}]},{"given":"Hongxu","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Wisconsin,Madison"}]},{"given":"Kangwook","family":"Lee","sequence":"additional","affiliation":[{"name":"University of Wisconsin,Madison"}]}],"member":"263","reference":[{"key":"ref13","article-title":"Fedfair: Training fair models in cross-silo federated learning","author":"chu","year":"2021"},{"key":"ref12","article-title":"Enforcing fairness in private federated learning via the modified method of differential multipliers","author":"rodr\u00edguez-g\u00e1lvez","year":"2021"},{"key":"ref15","article-title":"Addressing algorithmic disparity and performance inconsistency in federated learning","author":"cui","year":"2021","journal-title":"Thirty-Fifth Conference on Neural Information Processing Systems"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611976700.21"},{"key":"ref20","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume":"54","author":"mcmahan","year":"0"},{"key":"ref11","article-title":"Fairfed: Enabling group fairness in federated learning","author":"ezzeldin","year":"2021"},{"key":"ref10","article-title":"Federated learning: Strategies for improving communication efficiency","author":"kone?ny","year":"2017"},{"key":"ref21","article-title":"On the convergence of fedavg on non-iid data","author":"li","year":"0"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052660"},{"key":"ref1","first-page":"3315","article-title":"Equality of opportunity in supervised learning","volume":"29","author":"hardt","year":"2016","journal-title":"Advances in Neural Information Processing Systems (NIPS)"},{"key":"ref17","first-page":"107","article-title":"The cost of fairness in binary classification","author":"menon","year":"2018","journal-title":"Conference on Fairness Accountability and Transparency"},{"key":"ref16","article-title":"One-shot federated learning","author":"guha","year":"2019"},{"key":"ref19","article-title":"Inherent tradeoffs in learning fair representations","volume":"32","author":"zhao","year":"2019","journal-title":"Advances in Neural IInformation Processing Systems"},{"key":"ref18","article-title":"Unlocking fairness: a trade-off revisited","volume":"32","author":"wick","year":"2019","journal-title":"Advances in Neural IInformation Processing Systems"},{"key":"ref8","article-title":"Fairbatch: Batch selection for model fairness","author":"roh","year":"0"},{"key":"ref7","first-page":"962","article-title":"Fairness Constraints: Mechanisms for Fair Classification","volume":"54","author":"zafar","year":"2017","journal-title":"International Conference on Artificial Intelligence and Statistics (AISTATS)"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3278721.3278779"},{"key":"ref4","first-page":"228","article-title":"From parity to preference-based notions of fairness in classification","author":"zafar","year":"2017","journal-title":"Advances in Neural Information Processing Systems (NIPS) ser NIPS&#x2019;17"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/2090236.2090255"},{"key":"ref6","article-title":"On the (im)possibility of fairness","author":"friedler","year":"2016"},{"key":"ref5","first-page":"2564","article-title":"Preventing fairness gerrymandering: Auditing and learning for subgroup fairness","volume":"80","author":"kearns","year":"0"}],"event":{"name":"2023 IEEE International Symposium on Information Theory (ISIT)","location":"Taipei, Taiwan","start":{"date-parts":[[2023,6,25]]},"end":{"date-parts":[[2023,6,30]]}},"container-title":["2023 IEEE International Symposium on Information Theory (ISIT)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10206429\/10206441\/10206590.pdf?arnumber=10206590","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T20:27:34Z","timestamp":1773347254000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10206590\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,25]]},"references-count":21,"URL":"https:\/\/doi.org\/10.1109\/isit54713.2023.10206590","relation":{},"subject":[],"published":{"date-parts":[[2023,6,25]]}}}