In Byzantine-robust LDP distributed learning, generalization error decreases with increasing privacy strength in high-noise regimes but increases in low-noise regimes, shown via matching algorithmic stability bounds.
3.µ > µ min and n−f−1≥N min, where µmin and Nmin are constants that depends on assumption 1
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Unveiling the Non-Monotonic Effect of Privacy on Generalization under Byzantine Robustness
In Byzantine-robust LDP distributed learning, generalization error decreases with increasing privacy strength in high-noise regimes but increases in low-noise regimes, shown via matching algorithmic stability bounds.