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Communication-Efficient Online Federated Learning Framework for Nonlinear Regression
[article]
2021
arXiv
pre-print
Federated learning (FL) literature typically assumes that each client has a fixed amount of data, which is unrealistic in many practical applications. Some recent works introduced a framework for online FL (Online-Fed) wherein clients perform model learning on streaming data and communicate the model to the server; however, they do not address the associated communication overhead. As a solution, this paper presents a partial-sharing-based online federated learning framework (PSO-Fed) that
arXiv:2110.06556v1
fatcat:iirn6aqp4rg53ofe4vtqirtgty