Decentralized Stochastic

Decentralized stochastic methods address the challenge of optimizing functions across distributed networks of computing agents, aiming for efficient and robust solutions without a central coordinator. Current research focuses on improving the convergence speed and communication efficiency of algorithms like decentralized stochastic gradient descent (D-SGD) and its variants, often incorporating techniques like variance reduction and consensus-based approaches, sometimes within the framework of variational inequalities. These advancements are crucial for scalable machine learning, distributed control systems, and other applications requiring privacy-preserving collaborative computation across multiple devices or agents.