Why Federated Optimization Fails to Achieve Perfect Fitting? A Theoretical Perspective on Client-Side Optima
NeutralArtificial Intelligence
A recent paper on arXiv delves into the challenges of federated optimization, a method that allows for training global models without sharing client data. While current algorithms show theoretical convergence and stable training, the paper highlights the unclear reasons for performance drops when faced with data heterogeneity. This research is significant as it aims to clarify these issues, potentially leading to improved methods in distributed optimization.
— Curated by the World Pulse Now AI Editorial System





