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arXiv · 2511.18291

ADF-LoRA: Alternating Low-Rank Aggregation for Decentralized Federated Fine-Tuning

Xiaoyu Wang·Xiaotian Li·Zhixiang Zhou·Chen Li·Yong Liu
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Abstract

This paper revisits alternating low-rank updates for federated fine-tuning and examines their behavior in decentralized federated learning (DFL). While alternating the LoRA matrices has been shown to stabilize aggregation in centralized FL, extending this mechanism to decentralized, peer-to-peer communication introduces new challenges due to phase-state mismatch and block-wise divergence across clients. We introduce ADF-LoRA, which synchronizes the update of only one low-rank matrix per round and mixes both matrices to maintain more consistent parameter states under decentralized propagation. This design preserves the cross-term suppression effect of alternating updates while improving stability in serverless topologies. We provide a convergence analysis under standard smoothness assumptions and evaluate ADF-LoRA on multiple GLUE tasks. Experiments show that ADF-LoRA achieves faster and smoother convergence and delivers the highest average accuracy across tasks, outperforming existing LoRA variants in decentralized FL by a consistent margin.

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Xiaoyu Wang, Xiaotian Li, Zhixiang Zhou, Chen Li, Yong Liu. 2025-11-23. ADF-LoRA: Alternating Low-Rank Aggregation for Decentralized Federated Fine-Tuning. https://arxiv.org/abs/2511.18291

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