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

• Main Authors: Mohammadabadi, Seyed Mahmoud Sajjadi, Yang, Lei, Yan, Feng, Zhang, Junshan
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: arXiv 2024
• Subjects: Machine Learning cs.LG; Artificial Intelligence cs.AI; Distributed, Parallel, and Cluster Computing cs.DC; Multiagent Systems cs.MA; Performance cs.PF; FOS Computer and information sciences; DML
• Online Access: https://dx.doi.org/10.48550/arxiv.2405.00839; https://arxiv.org/abs/2405.00839

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 workload among agents through a decentralized approach. Leveraging local-loss split training, ComDML enables parallel updates, where slower agents offload part of their workload to faster agents. To minimize the overall training time, ComDML optimizes the workload balancing by jointly considering the communication and computation capacities of agents, which hinges upon integer programming. A dynamic ... : This paper has been accepted for presentation at ICDCS (44th IEEE International Conference on Distributed Computing Systems). Keywords: decentralized multi-agent learning, federated learning, edge computing, heterogeneous agents, workload balancing, and communication-efficient training ) ...