| [ | |
| "Theorem 3.1 bounds uniform stability degradation under Byzantine failure attacks (convex case) as 2γC^2T(1/((n-f)m) + √κ) with κ ≥ f/(n-2f), yielding an overall rate of O(√(f/(n-2f))) (Section 3.1, Theorem 3.1).", | |
| "Theorem 3.2 bounds stability degradation under data poisoning attacks with SMEA aggregation (convex case) as 2γC^2T(f/(n-f) + 1/((n-f)m)), giving a strictly better Θ(f/(n-f)) rate (Section 3.2, Theorem 3.2).", | |
| "Theorem 3.3 establishes a matching lower bound Ω(γC^2T(f/(n-f) + 1/((n-f)m))) for the data poisoning setting, proving the Θ(f/(n-f)) upper bound is tight (Section 3.2, Theorem 3.3).", | |
| "Shows Byzantine failures degrade uniform stability by a multiplicative factor of 2√(f/(n-f))·(1+f/(n-2f)) worse than data poisoning, with the gap widening as f approaches n/2 (Section 3, Theorems 3.1-3.3).", | |
| "Extends the convex-case bounds to the strongly convex setting, where Byzantine degradation is 2C^2/μ·(1/((n-f)m) + √κ) versus 2C^2/μ·(f/(n-f) + 1/((n-f)m)) for data poisoning (Section 3.1-3.2, Equations 2-4)." | |
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