Decentralized Nonconvex

Decentralized nonconvex optimization focuses on solving complex optimization problems where data is distributed across multiple agents, and the objective function is non-convex, lacking the convenient properties of convex functions. Current research emphasizes developing efficient algorithms that address communication bottlenecks inherent in distributed settings, often incorporating techniques like gradient tracking, communication compression, and variance reduction to improve convergence rates. These advancements are crucial for scaling machine learning models to massive datasets and for enabling privacy-preserving distributed computations, impacting fields like federated learning and distributed sensor networks.