RESEARCH · RESEARCH · #1152
Paper tightens convergence rates for federated variational inequalities and proposes LIPPAX
The paper refines analysis of Local Extra SGD to obtain tighter convergence guarantees for smooth monotone federated variational inequalities, identifies an inherent client-drift limitation of that method, and introduces a new algorithm — Local Inexact Proximal Point Algorithm with Extra Step (LIPPAX) — which provably mitigates drift and yields improved convergence rates in regimes such as bounded Hessian, bounded operator, and low-variance; results are also extended to federated composite variational inequalities.
KEY POINTS
- The paper refines analysis of Local Extra SGD to obtain tighter convergence guarantees for smooth monotone federated variational inequalities, identifies an inherent client-drift limitation of that method, and introduces a new algorithm — Local Inexact Proximal Point Algorithm with Extra Step (LIPPAX) — which provably mitigates drift and yields improved convergence rates in regimes such as bounded Hessian, bounded operator, and low-variance; results are also extended to federated composite variational inequalities.
- Improved theoretical convergence bounds and an algorithm that reduces client drift can make federated optimization for variational inequalities more efficient and closer to known federated convex-optimization rates.
- Faster Rates for Federated Variational Inequalities
WHY IT MATTERS
Improved theoretical convergence bounds and an algorithm that reduces client drift can make federated optimization for variational inequalities more efficient and closer to known federated convex-optimization rates.