Apple Researchers Improve Convergence Rates for Federated Variational Inequalities
Apple researchers published a paper establishing improved convergence rates for federated optimization of stochastic variational inequalities. They show the classical Local Extra SGD algorithm admits tighter guarantees under refined analysis for general smooth and monotone variational inequalities, narrowing the gap between existing federated VI convergence rates and state-of-the-art bounds known for federated convex optimization.
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