Web: http://arxiv.org/abs/2012.05625

Jan. 27, 2022, 2:11 a.m. | Canh T. Dinh, Nguyen H. Tran, Tuan Dung Nguyen, Wei Bao, Amir Rezaei Balef, Bing B. Zhou, Albert Y. Zomaya

cs.LG updates on arXiv.org arxiv.org

There is growing interest in applying distributed machine learning to edge
computing, forming federated edge learning. Federated edge learning faces
non-i.i.d. and heterogeneous data, and the communication between edge workers,
possibly through distant locations and with unstable wireless networks, is more
costly than their local computational overhead. In this work, we propose DONE,
a distributed approximate Newton-type algorithm with fast convergence rate for
communication-efficient federated edge learning. First, with strongly convex
and smooth loss functions, DONE approximates the Newton direction …

arxiv distributed edge learning type

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