FedREP: A Byzantine-Robust, Communication-Efficient and Privacy-Preserving Framework for Federated Learning. (arXiv:2303.05206v1 [cs.LG])

Federated learning (FL) has recently become a hot research topic, in which
Byzantine robustness, communication efficiency and privacy preservation are
three important aspects. However, the tension among these three aspects makes
it hard to simultaneously take all of them into account. In view of this
challenge, we theoretically analyze the conditions that a communication
compression method should satisfy to be compatible with existing
Byzantine-robust methods and privacy-preserving methods. Motivated by the
analysis results, we propose a novel communication compression method …

account challenge communication compression conditions efficiency federated learning framework hard hot important preservation privacy research robustness tension