FedALT: Federated Fine-Tuning through Adaptive Local Training with Rest-of-the-World LoRA

• URL: http://arxiv.org/abs/2503.11880v1
• Date: Fri, 14 Mar 2025 21:07:46 GMT
• Title: FedALT: Federated Fine-Tuning through Adaptive Local Training with Rest-of-the-World LoRA
• Authors: Jieming Bian, Lei Wang, Letian Zhang, Jie Xu,
• Abstract summary: Fine-tuning large language models (LLMs) in federated settings enables privacy-preserving adaptation but suffers from cross-client interference due to model aggregation.<n>We propose textbfFedALT, a novel personalized federated LoRA fine-tuning algorithm.<n>We show that FedALT significantly outperforms state-of-the-art personalized federated LoRA fine-tuning methods.
• Score: 5.162783756846019
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fine-tuning large language models (LLMs) in federated settings enables privacy-preserving adaptation but suffers from cross-client interference due to model aggregation. Existing federated LoRA fine-tuning methods, primarily based on FedAvg, struggle with data heterogeneity, leading to harmful cross-client interference and suboptimal personalization. In this work, we propose \textbf{FedALT}, a novel personalized federated LoRA fine-tuning algorithm that fundamentally departs from FedAvg. Instead of using an aggregated model to initialize local training, each client continues training its individual LoRA while incorporating shared knowledge through a separate Rest-of-the-World (RoTW) LoRA component. To effectively balance local adaptation and global information, FedALT introduces an adaptive mixer that dynamically learns input-specific weightings between the individual and RoTW LoRA components using the Mixture-of-Experts (MoE) principle. Through extensive experiments on NLP benchmarks, we demonstrate that FedALT significantly outperforms state-of-the-art personalized federated LoRA fine-tuning methods, achieving superior local adaptation without sacrificing computational efficiency.

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