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Conferences > OFC > 2024 > Tu3B > Tu3B.3

Network for AI: Communication-Efficient Federated Learning with MST-based Scheduling and Multi-Aggregation over Optical Networks

Ruikun Wang, Jiawei Zhang, Memedhe Ibrahimi, Zhiqun Gu, Yuming Xiao, Francesco Musumeci, Massimo Tornatore, and Yuefeng Ji

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Abstract

We propose a Minimum-Spanning-Tree-based scheduling and Multi-aggregation framework (MST-M) for communication-efficient Federated Learning. Simulation results show that MST-M saves over 10% in communication costs compared to existing heuristics.

© 2024 The Author(s)

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