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arXiv · 2507.05685

Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach

Xiaobing Chen·Boyang Zhang·Xiangwei Zhou·Mingxuan Sun·Shuai Zhang·Songyang Zhang·Geoffrey Ye Li
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Abstract

The integration of Federated Learning (FL) and Mixture-of-Experts (MoE) presents a compelling pathway for training more powerful, large-scale artificial intelligence models (LAMs) on decentralized data while preserving privacy. However, efficient federated training of these complex MoE-structured LAMs is hindered by significant system-level challenges, particularly in managing the interplay between heterogeneous client resources and the sophisticated coordination required for numerous specialized experts. This article highlights a critical, yet underexplored concept: the absence of robust quantitative strategies for dynamic client-expert alignment that holistically considers varying client capacities and the imperative for system-wise load balancing. Specifically, we propose a conceptual system design for intelligent client-expert alignment that incorporates dynamic fitness scoring, global expert load monitoring, and client capacity profiling. By tackling these systemic issues, we can unlock more scalable, efficient, and robust training mechanisms {with fewer communication rounds for convergence}, paving the way for the widespread deployment of large-scale federated MoE-structured LAMs in edge computing with ultra-high communication efficiency.

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Xiaobing Chen, Boyang Zhang, Xiangwei Zhou, Mingxuan Sun, Shuai Zhang, Songyang Zhang, Geoffrey Ye Li. 2025-07-08. Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach. https://arxiv.org/abs/2507.05685

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