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

Decentralized Multi-agent Learning (DML) enables collaborative model training while preserving data privacy. How-ever, inherent heterogeneity in agents' resources (computation, communication, and task size) may lead to substantial variations in training time. This heterogeneity creates a bottle...

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Veröffentlicht in:	Proceedings of the International Conference on Distributed Computing Systems S. 680 - 691
Hauptverfasser:	Sajjadi Mohammadabadi, Seyed Mahmoud, Yang, Lei, Yan, Feng, Zhang, Junshan
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 23.07.2024
Schlagworte:	Accuracy communication-efficient training Computational modeling Data models decentralized multi-agent learning edge computing Fasteners federated learning heterogeneous agents Integer programming Time measurement Training workload balancing
ISSN:	2575-8411
Online-Zugang:	Volltext
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