On Safeguarding Privacy and Security in the Framework of Federated Learning

14 Sep 2019Chuan MaJun LiMing DingHoward Hao YangFeng ShuTony Q. S. QuekH. Vincent Poor

Motivated by the advancing computational capacity of wireless end-user equipment (UE), as well as the increasing concerns about sharing private data, a new machine learning (ML) paradigm has emerged, namely federated learning (FL). Specifically, FL allows a decoupling of data provision at UEs and ML model aggregation at a central unit... (read more)

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