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pro vyhledávání: '"Chor, Romain"'
We investigate the generalization error of statistical learning models in a Federated Learning (FL) setting. Specifically, we study the evolution of the generalization error with the number of communication rounds $R$ between $K$ clients and a parame
Externí odkaz:
http://arxiv.org/abs/2306.05862
We study the generalization error of statistical learning models in a Federated Learning (FL) setting. Specifically, there are $K$ devices or clients, each holding an independent own dataset of size $n$. Individual models, learned locally via Stochas
Externí odkaz:
http://arxiv.org/abs/2304.12216
In this paper, we use tools from rate-distortion theory to establish new upper bounds on the generalization error of statistical distributed learning algorithms. Specifically, there are $K$ clients whose individually chosen models are aggregated by a
Externí odkaz:
http://arxiv.org/abs/2206.02604
We investigate the generalization error of statistical learning models in a Federated Learning (FL) setting. Specifically, we study the evolution of the generalization error with the number of communication rounds between the clients and the paramete
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a92a53267237417dbb5dfb045f993744
http://arxiv.org/abs/2306.05862
http://arxiv.org/abs/2306.05862