{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T22:53:43Z","timestamp":1781823223429,"version":"3.54.5"},"reference-count":32,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"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":[[2022,7,18]]},"DOI":"10.1109\/ijcnn55064.2022.9892211","type":"proceedings-article","created":{"date-parts":[[2022,9,30]],"date-time":"2022-09-30T19:56:04Z","timestamp":1664567764000},"page":"1-8","source":"Crossref","is-referenced-by-count":13,"title":["A Fair Federated Learning Framework With Reinforcement Learning"],"prefix":"10.1109","author":[{"given":"Yaqi","family":"Sun","sequence":"first","affiliation":[{"name":"Tsinghua Shenzhen International Graduate School,Shenzhen,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shijing","family":"Si","sequence":"additional","affiliation":[{"name":"Ping An Technology (Shenzhen) Co., Ltd.,Shenzhen,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianzong","family":"Wang","sequence":"additional","affiliation":[{"name":"Ping An Technology (Shenzhen) Co., Ltd.,Shenzhen,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuhan","family":"Dong","sequence":"additional","affiliation":[{"name":"Tsinghua Shenzhen International Graduate School,Shenzhen,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhitao","family":"Zhu","sequence":"additional","affiliation":[{"name":"Ping An Technology (Shenzhen) Co., Ltd.,Shenzhen,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Xiao","sequence":"additional","affiliation":[{"name":"Ping An Technology (Shenzhen) Co., Ltd.,Shenzhen,China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref32","article-title":"Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms","volume":"abs 1708 7747","author":"xiao","year":"2017","journal-title":"ArXiv"},{"key":"ref31","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"0","journal-title":"Proceedings of Neural Information Processing Systems"},{"key":"ref30","article-title":"Policy gradient methods for reinforcement learning with function approximation","author":"sutton","year":"0","journal-title":"Proceedings of Neural Information Processing Systems"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2021.3058573"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref12","article-title":"Federated learning: Strategies for improving communication efficiency","author":"konecny","year":"0","journal-title":"Proceedings of Neural Information Processing Systems"},{"key":"ref13","first-page":"5132","article-title":"Scaffold: Stochastic controlled averaging for on-device federated learning","author":"karimireddy","year":"0","journal-title":"Proceedings of International Conference on Machine Learning"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ISPDC52870.2021.9521631"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/67"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.07.098"},{"key":"ref17","article-title":"The non-iid data quagmire of decentralized machine learning","volume":"abs 1910 189","author":"hsieh","year":"0","journal-title":"Proceedings of the International Conference on Machine Learning"},{"key":"ref18","article-title":"Federated optimization in heterogeneous networks","author":"sahu","year":"2020","journal-title":"arXiv Learning"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/352"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3064351"},{"key":"ref4","first-page":"1005","article-title":"One for one, or all for all: Equilibria and optimality of collaboration in federated learning","author":"avrim","year":"0","journal-title":"Proceedings of International Conference on Machine Learning"},{"key":"ref27","first-page":"1278","article-title":"Stochastic backpropagation and approximate inference in deep generative models","author":"rezende","year":"0","journal-title":"Proceedings of International Conference on Machine Learning"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i6.16669"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/2090236.2090255"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3095915"},{"key":"ref5","article-title":"Optimization with non-differentiable constraints with applications to fairness, recall, churn, and other goals","volume":"abs 1809 4198","author":"andrew","year":"2019","journal-title":"ArXiv"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155494"},{"key":"ref7","article-title":"Addressing algorithmic disparity and performance inconsistency in federated learning","volume":"34","author":"cui","year":"0","journal-title":"Proceedings of Neural Information Processing Systems"},{"key":"ref2","article-title":"Fjord: Fair and accurate federated learning under heterogeneous targets with ordered dropout","volume":"34","author":"samuel","year":"0","journal-title":"Proceedings of Neural Information Processing Systems"},{"key":"ref9","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mc mahan","year":"2017","journal-title":"AISTATS"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449851"},{"key":"ref20","article-title":"Measuring the effects of non-identical data distribution for federated visual classification","volume":"abs 1909 6335","author":"hsu","year":"2019","journal-title":"ArXiv"},{"key":"ref22","first-page":"6357","article-title":"Ditto: Fair and robust federated learning through personalization","author":"li","year":"0","journal-title":"Proceedings of the International Conference on Machine Learning"},{"key":"ref21","article-title":"Adaptive federated optimization","author":"reddi","year":"0","journal-title":"Proceedings of International Conference on Learning Representations"},{"key":"ref24","article-title":"Fair resource allocation in federated learning","author":"li","year":"0","journal-title":"Proceedings of International Conference on Learning Representations"},{"key":"ref23","first-page":"4615","article-title":"Agnostic federated learning","author":"mohri","year":"0","journal-title":"Proceedings of International Conference on Machine Learning"},{"key":"ref26","article-title":"Categorical reparameterization with gumbel-softmax","author":"jang","year":"0","journal-title":"Proceedings of International Conference on Learning Representations"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/223"}],"event":{"name":"2022 International Joint Conference on Neural Networks (IJCNN)","location":"Padua, Italy","start":{"date-parts":[[2022,7,18]]},"end":{"date-parts":[[2022,7,23]]}},"container-title":["2022 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9891857\/9889787\/09892211.pdf?arnumber=9892211","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T23:01:05Z","timestamp":1667516465000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9892211\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,18]]},"references-count":32,"URL":"https:\/\/doi.org\/10.1109\/ijcnn55064.2022.9892211","relation":{},"subject":[],"published":{"date-parts":[[2022,7,18]]}}}