Server Side

Server-side optimization in federated learning focuses on improving the efficiency and stability of model training by strategically managing computations and updates at the central server. Current research emphasizes developing novel server-side algorithms, such as adaptive optimizers and second-order methods, to address challenges posed by data heterogeneity and communication bottlenecks, often incorporating techniques like extrapolation and careful step-size control. These advancements aim to accelerate convergence, reduce communication costs, and enhance the overall performance of federated learning systems, impacting both the theoretical understanding and practical deployment of this privacy-preserving machine learning paradigm.