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[2501.14406] Adaptive Rank Allocation for Federated Parameter-Efficient Fine-Tuning of Language Models
Summary
The paper presents FedARA, an innovative framework for federated parameter-efficient fine-tuning of language models, addressing data heterogeneity and communication inefficiencies in distributed training.
Why It Matters
As language models become integral to various applications, optimizing their fine-tuning in federated settings is crucial for enhancing privacy and efficiency. This research provides a solution to common challenges faced in distributed training, making it relevant for developers and researchers in AI and machine learning.
Key Takeaways
- FedARA improves fine-tuning efficiency in federated learning environments.
- It addresses data heterogeneity through truncated SVD adaptation.
- Dynamic rank allocation enhances communication efficiency.
- The framework reduces local computational costs and memory usage.
- Experimental results show significant improvements in training time and energy consumption.
Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2501.14406 (cs) [Submitted on 24 Jan 2025 (v1), last revised 18 Feb 2026 (this version, v4)] Title:Adaptive Rank Allocation for Federated Parameter-Efficient Fine-Tuning of Language Models Authors:Fei Wu, Jia Hu, Geyong Min, Shiqiang Wang View a PDF of the paper titled Adaptive Rank Allocation for Federated Parameter-Efficient Fine-Tuning of Language Models, by Fei Wu and 3 other authors View PDF HTML (experimental) Abstract:Pre-trained Language Models (PLMs) have demonstrated their superiority and versatility in modern Natural Language Processing (NLP), effectively adapting to various downstream tasks through further fine-tuning. Federated Parameter-Efficient Fine-Tuning (FedPEFT) has emerged as a promising solution to address privacy and efficiency challenges in distributed training for PLMs on resource-constrained local devices. However, our measurements reveal two key limitations of FedPEFT: heterogeneous data across devices exacerbates performance degradation of low-rank adaptation, and a fixed parameter configuration results in communication inefficiency. To overcome these limitations, we propose FedARA, a novel adaptive rank allocation framework for federated parameter-efficient fine-tuning of language models. Specifically, FedARA employs truncated Singular Value Decomposition (SVD) adaptation to enhance similar feature representation across clients, significantly mitigating the adverse effects of...