LoDAdaC: a unified local training-based decentralized framework with adaptive gradients and compressed communication

ArXi:2604.09970v1 Announce Type: new In the decentralized distributed learning, achieving fast convergence and low communication cost is essential for scalability and high efficiency. Adaptive gradient methods, such as Adam, have nstrated strong practical performance in deep learning and centralized distributed settings. However, their convergence properties remain largely unexplored in decentralized settings involving multiple local