{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T16:49:12Z","timestamp":1779295752513,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":40,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T00:00:00Z","timestamp":1723420800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Natural Science Fundation of China","award":["62202300"],"award-info":[{"award-number":["62202300"]}]},{"name":"Science and Technology Commission of Shanghai Municipality","award":["22PJ1404600"],"award-info":[{"award-number":["22PJ1404600"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,12]]},"DOI":"10.1145\/3673038.3673049","type":"proceedings-article","created":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T18:29:01Z","timestamp":1723141741000},"page":"494-503","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["FedCA: Efficient Federated Learning with Client Autonomy"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-9458-0458","authenticated-orcid":false,"given":"Na","family":"Lv","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5072-3458","authenticated-orcid":false,"given":"Zhi","family":"Shen","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9480-5632","authenticated-orcid":false,"given":"Chen","family":"Chen","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7024-441X","authenticated-orcid":false,"given":"Zhifeng","family":"Jiang","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5023-3952","authenticated-orcid":false,"given":"Jiayi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5832-0347","authenticated-orcid":false,"given":"Quan","family":"Chen","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0034-2302","authenticated-orcid":false,"given":"Minyi","family":"Guo","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,8,12]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"[n. d.]. RPyC. https:\/\/rpyc.readthedocs.io\/en\/latest\/."},{"key":"e_1_3_2_1_2_1","unstructured":"2020. wondershaper. https:\/\/github.com\/magnific0\/wondershaper."},{"key":"e_1_3_2_1_3_1","volume-title":"Refl: Resource-efficient federated learning. In ACM Eurosys.","author":"Abdelmoniem M","year":"2023","unstructured":"Ahmed\u00a0M Abdelmoniem, Atal\u00a0Narayan Sahu, Marco Canini, and Suhaib\u00a0A Fahmy. 2023. Refl: Resource-efficient federated learning. In ACM Eurosys."},{"key":"e_1_3_2_1_4_1","volume-title":"QSGD: Communication-efficient SGD via gradient quantization and encoding. In NeurIPS.","author":"Alistarh Dan","year":"2017","unstructured":"Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan Vojnovic. 2017. QSGD: Communication-efficient SGD via gradient quantization and encoding. In NeurIPS."},{"key":"e_1_3_2_1_5_1","volume-title":"Communication-efficient federated learning with adaptive parameter freezing","author":"Chen Chen","unstructured":"Chen Chen, Hong Xu, Wei Wang, Baochun Li, Bo Li, Li Chen, and Gong Zhang. 2021. Communication-efficient federated learning with adaptive parameter freezing. In IEEE ICDCS."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2023.3242721"},{"key":"e_1_3_2_1_7_1","volume-title":"Tictac: Accelerating distributed deep learning with communication scheduling. In MLSys.","author":"Hashemi Sayed\u00a0Hadi","year":"2019","unstructured":"Sayed\u00a0Hadi Hashemi, Sangeetha Abdu\u00a0Jyothi, and Roy Campbell. 2019. Tictac: Accelerating distributed deep learning with communication scheduling. In MLSys."},{"key":"e_1_3_2_1_8_1","unstructured":"Kevin Hsieh Aaron Harlap Nandita Vijaykumar Dimitris Konomis Gregory\u00a0R Ganger Phillip\u00a0B Gibbons and Onur Mutlu. 2017. Gaia:Geo-Distributed machine learning approaching LAN speeds. In USENIX NSDI."},{"key":"e_1_3_2_1_9_1","volume-title":"Papaya: Practical, private, and scalable federated learning. MLSys","author":"Huba Dzmitry","year":"2022","unstructured":"Dzmitry Huba, John Nguyen, Kshitiz Malik, Ruiyu Zhu, Mike Rabbat, Ashkan Yousefpour, Carole-Jean Wu, Hongyuan Zhan, Pavel Ustinov, Harish Srinivas, 2022. Papaya: Practical, private, and scalable federated learning. MLSys (2022)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Zhifeng Jiang Wei Wang Baochun Li and Bo Li. 2022. Pisces: efficient federated learning via guided asynchronous training. In ACM SoCC.","DOI":"10.1145\/3542929.3563463"},{"key":"e_1_3_2_1_11_1","volume-title":"Scaffold: Stochastic controlled averaging for federated learning. In ICML.","author":"Karimireddy Sai\u00a0Praneeth","year":"2020","unstructured":"Sai\u00a0Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda\u00a0Theertha Suresh. 2020. Scaffold: Stochastic controlled averaging for federated learning. In ICML."},{"key":"e_1_3_2_1_12_1","volume-title":"Federated Learning: Strategies for Improving Communication Efficiency. arxiv:1610.05492","author":"Kone\u010dn\u00fd Jakub","year":"2017","unstructured":"Jakub Kone\u010dn\u00fd, H.\u00a0Brendan McMahan, Felix\u00a0X. Yu, Peter Richt\u00e1rik, Ananda\u00a0Theertha Suresh, and Dave Bacon. 2017. Federated Learning: Strategies for Improving Communication Efficiency. arxiv:1610.05492"},{"key":"e_1_3_2_1_13_1","unstructured":"Alex Krizhevsky Geoffrey Hinton 2009. Learning multiple layers of features from tiny images. (2009)."},{"key":"e_1_3_2_1_14_1","volume-title":"Fedscale: Benchmarking model and system performance of federated learning at scale. In ICML.","author":"Lai Fan","year":"2022","unstructured":"Fan Lai, Yinwei Dai, Sanjay Singapuram, Jiachen Liu, Xiangfeng Zhu, Harsha Madhyastha, and Mosharaf Chowdhury. 2022. Fedscale: Benchmarking model and system performance of federated learning at scale. In ICML."},{"key":"e_1_3_2_1_15_1","volume-title":"Oort: Efficient federated learning via guided participant selection. In USENIX OSDI.","author":"Lai Fan","year":"2021","unstructured":"Fan Lai, Xiangfeng Zhu, Harsha\u00a0V Madhyastha, and Mosharaf Chowdhury. 2021. Oort: Efficient federated learning via guided participant selection. In USENIX OSDI."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_1_17_1","unstructured":"Sunwoo Lee Tuo Zhang and A\u00a0Salman Avestimehr. 2023. Layer-wise adaptive model aggregation for scalable federated learning. In AAAI."},{"key":"e_1_3_2_1_18_1","volume-title":"SmartPC: Hierarchical pace control in real-time federated learning system","author":"Li Li","unstructured":"Li Li, Haoyi Xiong, Zhishan Guo, Jun Wang, and Cheng-Zhong Xu. 2019. SmartPC: Hierarchical pace control in real-time federated learning system. In IEEE RTSS."},{"key":"e_1_3_2_1_19_1","volume-title":"Federated optimization in heterogeneous networks. MLSys","author":"Li Tian","year":"2020","unstructured":"Tian Li, Anit\u00a0Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2020. Federated optimization in heterogeneous networks. MLSys (2020)."},{"key":"e_1_3_2_1_20_1","volume-title":"Client-edge-cloud hierarchical federated learning","author":"Liu Lumin","unstructured":"Lumin Liu, Jun Zhang, SH Song, and Khaled\u00a0B Letaief. 2020. Client-edge-cloud hierarchical federated learning. In IEEE ICC."},{"key":"e_1_3_2_1_21_1","volume-title":"CMFL: Mitigating communication overhead for federated learning","author":"Luping WANG","year":"2019","unstructured":"WANG Luping, WANG Wei, and LI Bo. 2019. CMFL: Mitigating communication overhead for federated learning. In IEEE ICDCS."},{"key":"e_1_3_2_1_22_1","volume-title":"Communication-efficient learning of deep networks from decentralized data. arXiv preprint arXiv:1602.05629","author":"McMahan H\u00a0Brendan","year":"2016","unstructured":"H\u00a0Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, 2016. Communication-efficient learning of deep networks from decentralized data. arXiv preprint arXiv:1602.05629 (2016)."},{"key":"e_1_3_2_1_23_1","unstructured":"John Nguyen Kshitiz Malik Hongyuan Zhan Ashkan Yousefpour Mike Rabbat Mani Malek and Dzmitry Huba. 2022. Federated Learning with Buffered Asynchronous Aggregation. In AISTATS."},{"key":"e_1_3_2_1_24_1","volume-title":"d.]. Client selection for federated learning with heterogeneous resources in mobile edge","author":"Nishio Takayuki","unstructured":"Takayuki Nishio and Ryo Yonetani. [n. d.]. Client selection for federated learning with heterogeneous resources in mobile edge. In IEEE ICC."},{"key":"e_1_3_2_1_25_1","volume-title":"Adaptive federated optimization. arXiv preprint arXiv:2003.00295","author":"Reddi Sashank","year":"2020","unstructured":"Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Kone\u010dn\u1ef3, Sanjiv Kumar, and H\u00a0Brendan McMahan. 2020. Adaptive federated optimization. arXiv preprint arXiv:2003.00295 (2020)."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2022.3189320"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737367"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Jaemin Shin Yuanchun Li Yunxin Liu and Sung-Ju Lee. 2022. FedBalancer: data and pace control for efficient federated learning on heterogeneous clients. In ACM Mobisys.","DOI":"10.1145\/3498361.3538917"},{"key":"e_1_3_2_1_29_1","unstructured":"Zhenheng Tang Yonggang Zhang Shaohuai Shi Xin He Bo Han and Xiaowen Chu. 2022. Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning. In ICML."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2019.2904348"},{"key":"e_1_3_2_1_31_1","volume-title":"Resource-efficient federated learning with hierarchical aggregation in edge computing","author":"Wang Zhiyuan","unstructured":"Zhiyuan Wang, Hongli Xu, Jianchun Liu, He Huang, Chunming Qiao, and Yangming Zhao. 2021. Resource-efficient federated learning with hierarchical aggregation in edge computing. In IEEE INFOCOM."},{"key":"e_1_3_2_1_32_1","volume-title":"Speech commands: A dataset for limited-vocabulary speech recognition. arXiv preprint arXiv:1804.03209","author":"Warden Pete","year":"2018","unstructured":"Pete Warden. 2018. Speech commands: A dataset for limited-vocabulary speech recognition. arXiv preprint arXiv:1804.03209 (2018)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2020.2994391"},{"key":"e_1_3_2_1_34_1","unstructured":"Xidong Wu Feihu Huang Zhengmian Hu and Heng Huang. 2023. Faster adaptive federated learning. In AAAI."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.ENG.2016.02.008"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"crossref","unstructured":"Chengxu Yang Qipeng Wang Mengwei Xu Zhenpeng Chen Kaigui Bian Yunxin Liu and Xuanzhe Liu. 2021. Characterizing impacts of heterogeneity in federated learning upon large-scale smartphone data. In WWW.","DOI":"10.1145\/3442381.3449851"},{"key":"e_1_3_2_1_37_1","volume-title":"Wide residual networks. arXiv preprint arXiv:1605.07146","author":"Zagoruyko Sergey","year":"2016","unstructured":"Sergey Zagoruyko and Nikos Komodakis. 2016. Wide residual networks. arXiv preprint arXiv:1605.07146 (2016)."},{"key":"e_1_3_2_1_38_1","volume-title":"Adadelta: an adaptive learning rate method. arXiv preprint arXiv:1212.5701","author":"Zeiler D","year":"2012","unstructured":"Matthew\u00a0D Zeiler. 2012. Adadelta: an adaptive learning rate method. arXiv preprint arXiv:1212.5701 (2012)."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-021-00989-x"},{"key":"e_1_3_2_1_40_1","volume-title":"Federated learning with non-iid data. arXiv preprint arXiv:1806.00582","author":"Zhao Yue","year":"2018","unstructured":"Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra. 2018. Federated learning with non-iid data. arXiv preprint arXiv:1806.00582 (2018)."}],"event":{"name":"ICPP '24: the 53rd International Conference on Parallel Processing","location":"Gotland Sweden","acronym":"ICPP '24"},"container-title":["Proceedings of the 53rd International Conference on Parallel Processing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3673038.3673049","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3673038.3673049","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T17:30:48Z","timestamp":1758648648000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3673038.3673049"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,12]]},"references-count":40,"alternative-id":["10.1145\/3673038.3673049","10.1145\/3673038"],"URL":"https:\/\/doi.org\/10.1145\/3673038.3673049","relation":{},"subject":[],"published":{"date-parts":[[2024,8,12]]},"assertion":[{"value":"2024-08-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}