Toward Understanding the Tradeoff Between Privacy Preservation and Byzantine-Robustness in Decentralized Learning

Summary

This paper studies how privacy preservation and Byzantine-robustness interact in decentralized stochastic gradient descent, where honest-but-curious agents try to infer neighbors' data and Byzantine agents send wrong messages. The authors inject Gaussian noise for privacy and use robust aggregation rules against Byzantine attacks, and find a tradeoff: the noise worsens the learning error caused by defending against Byzantine attacks. They analyze how the "mixing abilities" of robust aggregation rules affect this tradeoff, and their theory is supported by numerical experiments.

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