Conflict-Aware Federated Fine-Tuning of Large Language Models with Mixture-of-Experts

• URL: http://arxiv.org/abs/2606.15625v1
• Date: Sun, 14 Jun 2026 06:22:09 GMT
• Title: Conflict-Aware Federated Fine-Tuning of Large Language Models with Mixture-of-Experts
• Abstract summary: Mixture-of-Experts (MoE) is a scalable alternative for efficient fine-tuning via sparse activation.<n>We propose FC-MoE, a federated conflict-aware framework for MoE fine-tuning.<n>It employs an importance aware weighting scheme to prioritize reliable local updates and utilizes gradient consensus projection to suppress conflicting updates.
• Score: 21.797282349662087
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The continuous scaling of large language models (LLMs) incurs prohibitive computational costs, making Mixture-of-Experts (MoE) a scalable alternative for efficient fine-tuning via sparse activation. While federated learning (FL) emerges as the paradigm for privacy-preserving collaborative optimization, integrating MoE into FL under data heterogeneity may trigger conflicting expert optimizations. Client-specific data distributions force same-indexed experts to optimize under inconsistent or even conflicting feature-label correlations. This mismatch induces destructive interference during aggregation, thus destabilizing the optimization trajectory and degrading model performance. To address this issue, we propose FC-MoE, a federated conflict-aware framework for MoE fine-tuning. It employs an importance aware weighting scheme to prioritize reliable local updates and utilizes gradient consensus projection to suppress conflicting updates, ensuring a stable global optimization path. Moreover, a local knowledge retention mechanism further preserves specialized client expertise by re-anchoring domain-specific residuals. Extensive experiments demonstrate that FC-MoE accelerates convergence and enhances both global and local model performance in non-IID federated environments.

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