Don't Reinvent the Wheel, Just Realign the Spokes: Resource-Efficient Federated Fine-Tuning via Rank-Wise Expert Assembly

Federated fine-tuning presents a promising avenue for adapting Large Language Models (LLMs) to downstream tasks while preserving data privacy. However, the prohibitive computational and communication overhead of LLM adaptation inhibits its deployment on resource-constrained edge devices. In this paper, we propose SMARTFED, a resource-efficient framework that circumvents expensive training from scratch by intelligently reusing knowledge embedded in existing LoRA modules. To fully exploit this potential and ensure scalability, we introduce the Mixture of Rank-Wise Experts (MoRE). MoRE decomposes LoRA modules into fine-grained rank-level experts, which are selectively activated based on input semantics and resource budgets. Furthermore, to optimize resource utilization, we propose Elastic Expert Quota Allocation (EEQA), a strategy that adaptively distributes expert capacity across parameter matrices based on their contribution to model performance. Extensive evaluations across multiple benchmarks demonstrate that SMARTFED significantly outperforms state-of-the-art methods in both model performance and training efficiency. Our code is publicly available at https://github.com/ benmagnifico/SmartFed.