  Federated learning (FL) is an emerging practical framework for effective and
scalable machine learning among multiple participants, such as end users,
organizations and companies. However, most existing FL or distributed learning
frameworks have not well addressed two important issues together: collaborative
fairness and adversarial robustness (e.g. free-riders and malicious
participants). In conventional FL, all participants receive the global model
(equal rewards), which might be unfair to the high-contributing participants.
Furthermore, due to the lack of a safeguard mechanism, free-riders or malicious
adversaries could game the system to access the global model for free or to
sabotage it. In this paper, we propose a novel Robust and Fair Federated
Learning (RFFL) framework to achieve collaborative fairness and adversarial
robustness simultaneously via a reputation mechanism. RFFL maintains a
reputation for each participant by examining their contributions via their
uploaded gradients (using vector similarity) and thus identifies
non-contributing or malicious participants to be removed. Our approach
differentiates itself by not requiring any auxiliary/validation dataset.
Extensive experiments on benchmark datasets show that RFFL can achieve high
fairness and is very robust to different types of adversaries while achieving
competitive predictive accuracy.

Machine Learning;Machine Learning