  Federated Learning (FL) is a machine learning paradigm to conduct
collaborative learning among clients on a joint model. The primary goal is to
share clients' local training parameters with an integrating server while
preserving their privacy. This method permits to exploit the potential of
massive mobile users' data for the benefit of machine learning models'
performance while keeping sensitive data on local devices. On the downside, FL
raises security and privacy concerns that have just started to be studied. To
address some of the key threats in FL, researchers have proposed to use secure
aggregation methods (e.g. homomorphic encryption, secure multiparty
computation, etc.). These solutions improve some security and privacy metrics,
but at the same time bring about other serious threats such as poisoning
attacks, backdoor attacks, and free running attacks. This paper proposes a new
defense method against poisoning attacks in FL called SaFL (Sybil-aware
Federated Learning) that minimizes the effect of sybils with a novel
time-variant aggregation scheme.
