FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge
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| Format: | Preprint |
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2025
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| _version_ | 1866914005927329792 |
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| author | Hu, Gang Teng, Yinglei Wu, Pengfei Wang, Nan |
| author_facet | Hu, Gang Teng, Yinglei Wu, Pengfei Wang, Nan |
| contents | As FMs drive progress toward Artificial General Intelligence (AGI), fine-tuning them under privacy and resource constraints has become increasingly critical particularly when highquality training data resides on distributed edge devices. Federated Learning (FL) offers a compelling solution through Federated Fine-Tuning (FFT), which enables collaborative model adaptation without sharing raw data. Recent approaches incorporate Parameter-Efficient Fine-Tuning (PEFT) techniques such as Low Rank Adaptation (LoRA) to reduce computational overhead. However, LoRA-based FFT faces two major limitations in heterogeneous FL environments: structural incompatibility across clients with varying LoRA configurations and limited adaptability to non-IID data distributions, which hinders convergence and generalization. To address these challenges, we propose FFT MoE, a novel FFT framework that replaces LoRA with sparse Mixture of Experts (MoE) adapters. Each client trains a lightweight gating network to selectively activate a personalized subset of experts, enabling fine-grained adaptation to local resource budgets while preserving aggregation compatibility. To further combat the expert load imbalance caused by device and data heterogeneity, we introduce a heterogeneity-aware auxiliary loss that dynamically regularizes the routing distribution to ensure expert diversity and balanced utilization. Extensive experiments spanning both IID and non-IID conditions demonstrate that FFT MoE consistently outperforms state of the art FFT baselines in generalization performance and training efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_18663 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge Hu, Gang Teng, Yinglei Wu, Pengfei Wang, Nan Machine Learning Artificial Intelligence As FMs drive progress toward Artificial General Intelligence (AGI), fine-tuning them under privacy and resource constraints has become increasingly critical particularly when highquality training data resides on distributed edge devices. Federated Learning (FL) offers a compelling solution through Federated Fine-Tuning (FFT), which enables collaborative model adaptation without sharing raw data. Recent approaches incorporate Parameter-Efficient Fine-Tuning (PEFT) techniques such as Low Rank Adaptation (LoRA) to reduce computational overhead. However, LoRA-based FFT faces two major limitations in heterogeneous FL environments: structural incompatibility across clients with varying LoRA configurations and limited adaptability to non-IID data distributions, which hinders convergence and generalization. To address these challenges, we propose FFT MoE, a novel FFT framework that replaces LoRA with sparse Mixture of Experts (MoE) adapters. Each client trains a lightweight gating network to selectively activate a personalized subset of experts, enabling fine-grained adaptation to local resource budgets while preserving aggregation compatibility. To further combat the expert load imbalance caused by device and data heterogeneity, we introduce a heterogeneity-aware auxiliary loss that dynamically regularizes the routing distribution to ensure expert diversity and balanced utilization. Extensive experiments spanning both IID and non-IID conditions demonstrate that FFT MoE consistently outperforms state of the art FFT baselines in generalization performance and training efficiency. |
| title | FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2508.18663 |