{"intvolume":"       238","quality_controlled":"1","_id":"17093","external_id":{"arxiv":["2206.10032"]},"publication_identifier":{"eissn":["2640-3498"]},"author":[{"id":"653bd8b6-f394-11eb-9cf6-c0bbf6cd78d4","last_name":"Zakerinia","full_name":"Zakerinia, Hossein","first_name":"Hossein"},{"last_name":"Talaei","full_name":"Talaei, Shayan","first_name":"Shayan"},{"first_name":"Giorgi","last_name":"Nadiradze","full_name":"Nadiradze, Giorgi","orcid":"0000-0001-5634-0731","id":"3279A00C-F248-11E8-B48F-1D18A9856A87"},{"id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-3650-940X","full_name":"Alistarh, Dan-Adrian","last_name":"Alistarh","first_name":"Dan-Adrian"}],"date_created":"2024-06-02T22:00:57Z","oa":1,"alternative_title":["PMLR"],"publication_status":"published","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2206.10032","open_access":"1"}],"department":[{"_id":"DaAl"},{"_id":"ChLa"}],"page":"3448-3456","article_processing_charge":"No","title":"Communication-efficient federated learning with data and client heterogeneity","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","month":"05","oa_version":"Preprint","date_published":"2024-05-01T00:00:00Z","volume":238,"year":"2024","language":[{"iso":"eng"}],"type":"conference","abstract":[{"lang":"eng","text":"Federated Learning (FL) enables large-scale distributed training of machine learning models, while still allowing individual nodes to maintain data locally. However, executing FL at scale comes with inherent practical challenges: 1) heterogeneity of the local node data distributions, 2) heterogeneity of node computational speeds (asynchrony), but also 3) constraints in the amount of communication between the clients and the server. In this work, we present the first variant of the classic federated averaging (FedAvg) algorithm which, at the same time, supports data heterogeneity, partial client asynchrony, and communication compression. Our algorithm comes with a novel, rigorous analysis showing that, in spite of these system relaxations, it can provide similar convergence to FedAvg in interesting parameter regimes. Experimental results in the rigorous LEAF benchmark on setups of up to 300 nodes show that our algorithm ensures fast convergence for standard federated tasks, improving upon prior quantized and asynchronous approaches."}],"citation":{"apa":"Zakerinia, H., Talaei, S., Nadiradze, G., &#38; Alistarh, D.-A. (2024). Communication-efficient federated learning with data and client heterogeneity. In <i>Proceedings of the 27th International Conference on Artificial Intelligence and Statistics</i> (Vol. 238, pp. 3448–3456). Valencia, Spain: ML Research Press.","chicago":"Zakerinia, Hossein, Shayan Talaei, Giorgi Nadiradze, and Dan-Adrian Alistarh. “Communication-Efficient Federated Learning with Data and Client Heterogeneity.” In <i>Proceedings of the 27th International Conference on Artificial Intelligence and Statistics</i>, 238:3448–56. ML Research Press, 2024.","ama":"Zakerinia H, Talaei S, Nadiradze G, Alistarh D-A. Communication-efficient federated learning with data and client heterogeneity. In: <i>Proceedings of the 27th International Conference on Artificial Intelligence and Statistics</i>. Vol 238. ML Research Press; 2024:3448-3456.","ista":"Zakerinia H, Talaei S, Nadiradze G, Alistarh D-A. 2024. Communication-efficient federated learning with data and client heterogeneity. Proceedings of the 27th International Conference on Artificial Intelligence and Statistics. AISTATS: Conference on Artificial Intelligence and Statistics, PMLR, vol. 238, 3448–3456.","ieee":"H. Zakerinia, S. Talaei, G. Nadiradze, and D.-A. Alistarh, “Communication-efficient federated learning with data and client heterogeneity,” in <i>Proceedings of the 27th International Conference on Artificial Intelligence and Statistics</i>, Valencia, Spain, 2024, vol. 238, pp. 3448–3456.","short":"H. Zakerinia, S. Talaei, G. Nadiradze, D.-A. Alistarh, in:, Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, ML Research Press, 2024, pp. 3448–3456.","mla":"Zakerinia, Hossein, et al. “Communication-Efficient Federated Learning with Data and Client Heterogeneity.” <i>Proceedings of the 27th International Conference on Artificial Intelligence and Statistics</i>, vol. 238, ML Research Press, 2024, pp. 3448–56."},"conference":{"name":"AISTATS: Conference on Artificial Intelligence and Statistics","location":"Valencia, Spain","start_date":"2024-05-02","end_date":"2024-05-04"},"scopus_import":"1","day":"01","status":"public","publisher":"ML Research Press","corr_author":"1","date_updated":"2024-10-09T21:08:57Z","publication":"Proceedings of the 27th International Conference on Artificial Intelligence and Statistics"}