arXiv:2606.15625v1 Announce Type: new 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 mismatc
The continuous scaling and integration of LLMs with federated learning necessitate innovative solutions for efficient and privacy-preserving optimization, especially as data heterogeneity becomes a critical challenge.
This research addresses a core technical hurdle in scaling advanced AI models in a decentralized and privacy-preserving manner, crucial for both enterprise adoption and personal device intelligence.
The proposed 'Conflict-Aware Federated Fine-Tuning' method offers a pathway to more robust and efficient federated learning for LLMs, overcoming limitations posed by diverse client data distributions.
- · AI developers focused on privacy
- · Enterprises with sensitive data
- · Edge computing providers
- · Users of privacy-first AI applications
- · Centralized AI training paradigms
- · Less efficient federated learning approaches
Improved performance and broader adoption of federated LLMs across various distributed applications.
Accelerated development of privacy-preserving AI services, potentially expanding AI use cases in highly regulated industries.
Enhanced data sovereignty for individuals and organizations as models are trained without centralizing raw data.
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Read at arXiv cs.LG