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A Systematic Literature Review on Federated Learning: From A Model Quality Perspective
[article]
2020
arXiv
pre-print
As an emerging technique, Federated Learning (FL) can jointly train a global model with the data remaining locally, which effectively solves the problem of data privacy protection through the encryption mechanism. The clients train their local model, and the server aggregates models until convergence. In this process, the server uses an incentive mechanism to encourage clients to contribute high-quality and large-volume data to improve the global model. Although some works have applied FL to
arXiv:2012.01973v1
fatcat:64yt53gdavfavmi5puj4rflknm