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

arXiv: https://arxiv.org/abs/2507.05685 · HTML: https://mlsanity.com/paper/2507.05685/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Federated mixture-of-experts, one of 12 directions in Mixture-of-experts.
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 8 Jul 2025.

## Placed in

- Mixture-of-experts / [Federated mixture-of-experts](https://mlsanity.com/moe/federated-mixture-of-experts/)

## Builds on (most cited first, 6 of 6)

- [Federated Mixture of Experts](https://mlsanity.com/paper/2107.06724/) (2021)
- [FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts](https://mlsanity.com/paper/2408.11304/) (2024)
- [pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated Learning](https://mlsanity.com/paper/2402.01350/) (2024)
- [Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2410.10114/) (2024)
- [FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregation](https://mlsanity.com/paper/2411.02115/) (2024)
- [Learning to Specialize: Joint Gating-Expert Training for Adaptive MoEs in Decentralized Settings](https://mlsanity.com/paper/2306.08586/) (2023)

## Built on by (most cited first, 3 of 3)

- [FLEX-MoE: Federated Mixture-of-Experts with Load-balanced Expert Assignment for Edge Computing](https://mlsanity.com/paper/2512.23070/) (2025)
- [Towards Efficient Federated Learning of Networked Mixture-of-Experts for Mobile Edge Computing](https://mlsanity.com/paper/2511.01743/) (2025)
- [Fisher-Routed Mixture of Experts for Federated Class-Incremental Learning](https://mlsanity.com/paper/2606.28835/) (2026)

## Abstract (arXiv)

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.