Federated learning (FL) is a promising paradigm to enable collaborative model training with decentralized data. However, the training process of Large Language Models (LLMs) generally incurs the update of significant parameters, which limits the applicability of FL techniques to tackle the LLMs in real scenarios. Prompt tuning can significantly reduce the number of parameters to update, but it either incurs performance degradation or low training efficiency. The straightforward utilization of prompt tuning in the FL often raises non-trivial communication costs and dramatically degrades performance. In addition, the decentralized data is generally non-Independent and Identically Distributed (non-IID), which brings client drift problems and thus poor performance. This paper proposes a Parameter-efficient prompt Tuning approach with Adaptive Optimization, i.e., FedPepTAO, to enable efficient and effective FL of LLMs. First, an efficient partial prompt tuning approach is proposed to improve performance and efficiency simultaneously. Second, a novel adaptive optimization method is developed to address the client drift problems on both the device and server sides to enhance performance further. Extensive experiments based on 10 datasets demonstrate the superb performance (up to 60.8\% in terms of accuracy) and efficiency (up to 97.59\% in terms of training time) of FedPepTAO compared with 9 baseline approaches. Our code is available at https://github.com/llm-eff/FedPepTAO.

翻译：联邦学习（FL）是一种利用分散数据实现协同模型训练的前沿范式。然而，大语言模型（LLMs）的训练过程通常涉及大量参数更新，这限制了FL技术在真实场景中处理LLMs的可行性。提示调优虽能显著减少待更新参数数量，但可能导致性能下降或训练效率低下。在联邦学习中直接使用提示调优往往产生显著的通信开销并急剧降低性能。此外，分散数据通常呈现非独立同分布（non-IID）特性，这引发客户端漂移问题并导致性能劣化。本文提出一种带自适应优化的参数高效提示调优方法FedPepTAO，以实现LLM的高效高质联邦学习。首先，提出高效的部分提示调优策略以同步提升性能与效率；其次，开发新型自适应优化方法，在设备端和服务端双维度解决客户端漂移问题以进一步增强性能。基于10个数据集的广泛实验表明，与9种基线方法相比，FedPepTAO在准确率上最高提升60.8%，训练时间上最高减少97.59%。我们的代码已开源至https://github.com/llm-eff/FedPepTAO。