Communication-Efficient Training Workload Balancing for Decentralized Multi-Agent Learning

arXiv:2405.00839v1 Announce Type: new
Abstract: Decentralized Multi-agent Learning (DML) enables collaborative model training while preserving data privacy. However, inherent heterogeneity in agents' resources (computation, communication, and task size) may lead to substantial variations in training time. This heterogeneity creates a bottleneck, lengthening the overall training time due to straggler effects and potentially wasting spare resources of faster agents. To minimize training time in heterogeneous environments, we present a Communication-Efficient Training Workload Balancing for Decentralized Multi-Agent Learning (ComDML), which balances the …

abstract agent agents arxiv collaborative communication computation cs.ai cs.dc cs.lg cs.ma cs.pf data data privacy decentralized effects however multi-agent multi-agent learning privacy resources training type while