{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:06:38Z","timestamp":1750309598990,"version":"3.41.0"},"reference-count":8,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T00:00:00Z","timestamp":1742256000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGMETRICS Perform. Eval. Rev."],"published-print":{"date-parts":[[2025,3,18]]},"abstract":"<jats:p>Federated Learning (FL) enables clients (mobile or IoT devices) to train a shared machine learning model coordinated by a central server while keeping their data local, addressing communication and privacy concerns. In the FedAvg algorithm [2], clients perform multiple local stochastic gradient descent (SGD) steps on their datasets and send their model updates to the server. The server then aggregates these client updates to produce the new global model and sends this back to the clients for the subsequent iteration.<\/jats:p>","DOI":"10.1145\/3725536.3725542","type":"journal-article","created":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T22:19:36Z","timestamp":1742336376000},"page":"11-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["The Many Facets of Variance Reduction in Federated Learning"],"prefix":"10.1145","volume":"52","author":[{"given":"Angelo","family":"Rodio","sequence":"first","affiliation":[{"name":"Centre Inria d'Universit\u00c9 C\u00f4te d'Azur"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,3,18]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"FedStale: Leveraging Stale Client Updates in Federated Learning,\" in ECAI","author":"Rodio A.","year":"2024","unstructured":"A. Rodio and G. Neglia, \"FedStale: Leveraging Stale Client Updates in Federated Learning,\" in ECAI 2024, IOS Press, 2024."},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics","author":"McMahan B.","year":"2017","unstructured":"B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, \"Communication-Efficient Learning of Deep Networks from Decentralized Data,\" in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017."},{"key":"e_1_2_1_3_1","volume-title":"A Field Guide to Federated Optimization,\" arXiv:2107.06917 [cs]","author":"Wang J.","year":"2021","unstructured":"J. Wang et al., \"A Field Guide to Federated Optimization,\" arXiv:2107.06917 [cs], 2021."},{"key":"e_1_2_1_4_1","volume-title":"SCAFFOLD: Stochastic Controlled Averaging for Federated Learning,\" in Proceedings of the 37th International Conference on Machine Learning","author":"Karimireddy S. P.","year":"2020","unstructured":"S. P. Karimireddy, S. Kale, M. Mohri, S. Reddi, S. Stich, and A. T. Suresh, \"SCAFFOLD: Stochastic Controlled Averaging for Federated Learning,\" in Proceedings of the 37th International Conference on Machine Learning, 2020."},{"key":"e_1_2_1_5_1","volume-title":"A Unified Analysis of Federated Learning with Arbitrary Client Participation,\" Advances in Neural Information Processing Systems","author":"Wang S.","year":"2022","unstructured":"S. Wang and M. Ji, \"A Unified Analysis of Federated Learning with Arbitrary Client Participation,\" Advances in Neural Information Processing Systems, 2022."},{"key":"e_1_2_1_6_1","volume-title":"Fedvarp: Tackling the variance due to partial client participation in federated learning,\" in Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence","author":"Jhunjhunwala D.","year":"2022","unstructured":"D. Jhunjhunwala, P. Sharma, A. Nagarkatti, and G. Joshi, \"Fedvarp: Tackling the variance due to partial client participation in federated learning,\" in Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, 2022."},{"key":"e_1_2_1_7_1","author":"Scaman K.","year":"2019","unstructured":"K. Scaman, F. Bach, S. Bubeck, Y. T. Lee, and L. Massouli\u00e9, \"Optimal Convergence Rates for Convex Distributed Optimization in Networks,\" Journal of Machine Learning Research, 2019.","journal-title":"\"Optimal Convergence Rates for Convex Distributed Optimization in Networks,\" Journal of Machine Learning Research"},{"key":"e_1_2_1_8_1","doi-asserted-by":"crossref","unstructured":"L. Deng \"The MNIST Database of Handwritten Digit Images for Machine Learning Research \" IEEE Signal Processing 2012.","DOI":"10.1109\/MSP.2012.2211477"}],"container-title":["ACM SIGMETRICS Performance Evaluation Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3725536.3725542","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3725536.3725542","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:57:04Z","timestamp":1750298224000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3725536.3725542"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,18]]},"references-count":8,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,3,18]]}},"alternative-id":["10.1145\/3725536.3725542"],"URL":"https:\/\/doi.org\/10.1145\/3725536.3725542","relation":{},"ISSN":["0163-5999"],"issn-type":[{"type":"print","value":"0163-5999"}],"subject":[],"published":{"date-parts":[[2025,3,18]]},"assertion":[{"value":"2025-03-18","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}