  Federated learning (FL) enables multiple clients to collaboratively train an
accurate global model while protecting clients' data privacy. However, FL is
susceptible to Byzantine attacks from malicious participants. Although the
problem has gained significant attention, existing defenses have several flaws:
the server irrationally chooses malicious clients for aggregation even after
they have been detected in previous rounds; the defenses perform ineffectively
against sybil attacks or in the heterogeneous data setting.
  To overcome these issues, we propose MAB-RFL, a new method for robust
aggregation in FL. By modelling the client selection as an extended multi-armed
bandit (MAB) problem, we propose an adaptive client selection strategy to
choose honest clients that are more likely to contribute high-quality updates.
We then propose two approaches to identify malicious updates from sybil and
non-sybil attacks, based on which rewards for each client selection decision
can be accurately evaluated to discourage malicious behaviors. MAB-RFL achieves
a satisfying balance between exploration and exploitation on the potential
benign clients. Extensive experimental results show that MAB-RFL outperforms
existing defenses in three attack scenarios under different percentages of
attackers.
