Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach
On the map: Mixture-of-experts → Federated mixture-of-experts
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.
Key facts. Cited by 3 papers in the map (data through 7 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.
Builds on
6 of 14 references.
Closest ancestors · 5
Same direction, newest first
- 2024FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregation4 Nov
- 2024FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts21 Aug
- 2024pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated Learning2 Feb
- 2023Learning to Specialize: Joint Gating-Expert Training for Adaptive MoEs in Decentralized Settings14 Jun
- 2021Federated Mixture of Experts14 Jul
Other references · 1
Built on by
Citing papers per year: 2025: 2; 2026: 1.
3 papers.
- 2025FLEX-MoE: Federated Mixture-of-Experts with Load-balanced Expert Assignment for Edge Computingcited by 2
- 2025Towards Efficient Federated Learning of Networked Mixture-of-Experts for Mobile Edge Computingcited by 1
- 2026Fisher-Routed Mixture of Experts for Federated Class-Incremental Learningcited by 0
- 2026Fisher-Routed Mixture of Experts for Federated Class-Incremental Learning27 Jun 2026
- 2025FLEX-MoE: Federated Mixture-of-Experts with Load-balanced Expert Assignment for Edge Computing28 Dec 2025
- 2025Towards Efficient Federated Learning of Networked Mixture-of-Experts for Mobile Edge Computing3 Nov 2025
Similar, not cited
Close in wording, no citation either way
- 2026HFedMoE: Resource-aware Heterogeneous Federated Learning with Mixture-of-Experts
- 2026Aggregation Alignment for Federated Learning with Mixture-of-Experts under Data Heterogeneity
- 2026Conflict-Aware Federated Fine-Tuning of Large Language Models with Mixture-of-Experts
- 2025FLEx: Personalized Federated Learning for Mixture-of-Experts LLMs via Expert Grafting
- 2026FedCoE: Bridging Generalization and Personalization via Federated Coordinated Dual-level MoEs
- 2026MoE Router-Guided Clustering for Heterogeneous Federated Instruction Tuning
- 2026Scaling Multi-Node Mixture-of-Experts Inference Using Expert Activation Patterns
- 2025A Theoretical Framework for Auxiliary-Loss-Free Load Balancing of Sparse Mixture-of-Experts in Large-Scale AI Models