Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911045045452800 |
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| author | Chen, Xiaobing Zhang, Boyang Zhou, Xiangwei Sun, Mingxuan Zhang, Shuai Zhang, Songyang Li, Geoffrey Ye |
| author_facet | Chen, Xiaobing Zhang, Boyang Zhou, Xiangwei Sun, Mingxuan Zhang, Shuai Zhang, Songyang Li, Geoffrey Ye |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_05685 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach Chen, Xiaobing Zhang, Boyang Zhou, Xiangwei Sun, Mingxuan Zhang, Shuai Zhang, Songyang Li, Geoffrey Ye Machine Learning Artificial Intelligence 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. |
| title | Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2507.05685 |