用参数差值传输降低通信开销，实现高效私密大模型微调
GradualDiff-Fed: A Federated Learning Specialized Framework for Large Language Model
- 只传模型参数差异，而非完整模型，大幅减少通信量
- 在分布式环境下达到与集中训练相当的性能表现
- 适合医疗等需隐私保护的大模型微调场景
大型语言模型（LLM）的快速普及催生了对特定领域（如医学科学）模型微调的迫切需求。尽管联邦学习（FL）提供了一种无需共享原始数据即可协作微调大模型的去中心化、隐私保护方法，但仍面临性能下降和大模型参数量管理困难等挑战。本文提出GradualDiff-Fed，一种专为大模型设计的联邦学习框架，通过仅在训练轮次间传输模型权重差值，而非完整模型，显著降低通信成本。实验表明，该方法在保持与集中训练相当性能的同时，大幅减少通信开销，验证了其在隐私保护环境中高效微调大模型的可行性。
原文摘要 · Abstract (English)
The rapid proliferation of large language models (LLMs) has created an unprecedented demand for fine-tuning models for specialized domains, such as medical science. While federated learning (FL) offers a decentralized and privacy-preserving approach to collaboratively fine-tune LLMs without sharing raw data, it presents significant challenges, particularly in performance and managing large model sizes efficiently. In this paper, we introduce GradualDiff-Fed, an FL framework designed explicitly for LLMs, and their challenge of handling the high parameter size. GradualDiff-Fed reduces communication costs by transmitting only the difference of model weights rather than the entire model during training rounds. Such an approach significantly improves scalability and communication efficiency, making it more feasible to fine-tune LLMs across distributed clients without compromising performance. Our evaluation demonstrates that GradualDiff-Fed achieves performance on par with centralized training while drastically reducing communication overhead. These results highlight the potential of GradualDiff-Fed as an efficient solution for fine-tuning large models from distributed data in privacy-preserving settings without comprising performance.
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