Robust Aggregation for Federated Learning

Krishna Pillutla, Sham M. Kakade, Zaïd Harchaoui

2022 · IEEE Transactions on Signal Processing · 865 citations

We present a novel approach to federated learning that endows its aggregation process with greater robustness to potential poisoning of local data or model parameters of participating devices. The proposed approach, Robust Federated Aggregation (RFA), relies on the aggregation of updates using the geometric median, which can be computed efficiently using a Weiszfeld-type algorithm. RFA is agnostic to the level of corruption and aggregates model updates without revealing each device’s individual contribution. We establish the convergence of the robust federated learning algorithm for the stoch…

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