Výsledky vyhledávání - "Lavoie, E. (Erick)"

D-Cliques:Compensating for Data Heterogeneity with Topology in Decentralized Federated Learning

Autor: Bellet, A. (Aurelien), Kermarrec, A-M. (Anne-Marie), Lavoie, E. (Erick)

The convergence speed of machine learning models trained with Federated Learning is significantly affected by heterogeneous data partitions, even more so in a fully decentralized setting without a central server. In this paper, we show that the impac

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______4198::99dec12613d4f7240b3c3d466675ef4e
http://hdl.handle.net/20.500.12210/80019

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Refined Convergence and Topology Learning for Decentralized SGD with Heterogeneous Data

Autor: Bars, B.L. (Batiste Le), Bellet, A. (Aurelien), Tommasi, M. (Marc), Lavoie, E. (Erick), Kermarrec, A-M. (Anne-Marie)

One of the key challenges in decentralized and federated learning is to design algorithms that efficiently deal with highly heterogeneous data distributions across agents. In this paper, we revisit the analysis of the popular Decentralized Stochastic

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______4198::f0acb486ddd89d2087806600184ef655
http://hdl.handle.net/20.500.12210/80018

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