Federated Mixture-of-Experts Alignment on Mobile Edge Networks under Data Heterogeneity

arXiv:2603.21276v2 Announce Type: replace-cross Abstract: The growing demand for on-device large language model (LLM) services on mobile edge devices has driven the adoption of Mixture-of-Experts (MoE) architectures, which scale model capacity with limited computation. Since fine-tuning MoE-based LLMs relies on privacy-sensitive local data, federated learning (FL) offers a natural paradigm for collaborative training without exposing raw data. However, integrating MoE-based LLM fine-tuning into FL faces two critical challenges caused by data heterogeneity across clients: (i) divergent local dat

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