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Abstract

Robust distributed learning algorithms aim to maintain reliable performance despite the presence of misbehaving workers. Such misbehaviors are commonly modeled as Byzantine failures, allowing arbitrarily corrupted communication, or as data poisoning, a weaker form of corruption restricted to local training data. While prior work shows similar optimization guarantees for both models, an important question remains: How do these threat models impact generalization? We show, for the first time, a fundamental gap in generalization guarantees between the two threat models: Byzantine failures yield strictly worse rates than those achievable under data poisoning.