Apple ML research tightens convergence rates for federated variational inequalities
Apple ML Research · rss · 2026-09-28
Apple ML Research published Faster Rates for Federated Variational Inequalities, studying federated optimization for stochastic variational inequalities (VIs).
- Gap: despite growing attention, existing convergence rates lag the state-of-the-art bounds known for federated convex optimization.
- Result: with a refined analysis, the authors show the classical Local Extra SGD algorithm admits tighter guarantees for general smooth and monotone VIs, establishing a series of improved convergence rates.
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