Communication-Efficient Distributed Learning with Local Immediate Error Compensation

arXiv:2402.11857v1 Announce Type: new
Abstract: Gradient compression with error compensation has attracted significant attention with the target of reducing the heavy communication overhead in distributed learning. However, existing compression methods either perform only unidirectional compression in one iteration with higher communication cost, or bidirectional compression with slower convergence rate. In this work, we propose the Local Immediate Error Compensated SGD (LIEC-SGD) optimization algorithm to break the above bottlenecks based on bidirectional compression and carefully designed compensation approaches. Specifically, the bidirectional …

abstract arxiv attention communication compensation compression convergence cost cs.dc cs.lg distributed distributed learning error gradient iteration rate type work