most citedMAB-based Client Selection for Federated Learning with Uncertain Resources in Mobile Networks

4 citations · 4 across the 1 of their papers we have counts for

collaborators

2 papers

cs.NI2020★ 4 cited

MAB-based Client Selection for Federated Learning with Uncertain Resources in Mobile Networks

Naoya Yoshida, Takayuki Nishio, Masahiro Morikura +1

This paper proposes a client selection method for federated learning (FL) when the computation and communication resource of clients cannot be estimated; the method trains a machin…

cs.LG2019

Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Data

Naoya Yoshida, Takayuki Nishio, Masahiro Morikura +2

This paper proposes a cooperative mechanism for mitigating the performance degradation due to non-independent-and-identically-distributed (non-IID) data in collaborative machine le…