Decentralized Gradient

Decentralized gradient methods aim to collaboratively train machine learning models across distributed networks without a central server, prioritizing data privacy and efficiency. Current research emphasizes developing algorithms like decentralized gradient descent and gradient tracking, often incorporating local updates and asynchronous communication to improve convergence speed and robustness against data heterogeneity and network challenges. This area is significant for enabling large-scale machine learning applications in resource-constrained environments and enhancing data privacy, with ongoing work focusing on addressing communication efficiency, convergence guarantees, and vulnerabilities to privacy attacks.