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Bayesian Optimizationdifferentiable convex optimizationBayesian Multiobjective OptimizationPareto Optimal Policy OptimizationAccelerated Decentralized Stochastic Gradient Descent for Strongly Convex OptimizationDenoising Diffusion Policy Optimizationdistributed trainingPrivate Stochastic Convex OptimizationProximal Policy Optimizationoptimization theoryDirect Preference Optimization (DPO)genetic optimizer
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Tight Convergence Theory for Error Feedback Algorithms in Distributed Optimization
This paper provides tight convergence analyses for two major error-feedback algorithms—classic Error Feedback (EF) and Error Feedback 21 (EF21)—used to mitigate communication bottlenecks in distributed learning. The authors identify optimal step-size choices and construct tailored Lyapunov functions for each method, yielding guarantees that hold independently of the number of agents and recover the best known single-agent bounds. The work clarifies the relative performance of these gradient compression variants, which has remained poorly understood despite widespread use.