zPROBE: Zero Peek Robustness Checks for Federated Learning. (arXiv:2206.12100v1 [cs.LG])

Privacy-preserving federated learning allows multiple users to jointly train
a model with coordination of a central server. The server only learns the final
aggregation result, thereby preventing leakage of the users' (private) training
data from the individual model updates. However, keeping the individual updates
private allows malicious users to perform Byzantine attacks and degrade the
model accuracy without being detected. Best existing defenses against Byzantine
workers rely on robust rank-based statistics, e.g., the median, to find
malicious updates. However, implementing …

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