Related papers: pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning

pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning

URL: http://arxiv.org/abs/2310.13283v2
Date: Sun, 11 Feb 2024 11:38:38 GMT
Title: pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning
Authors: Liping Yi, Han Yu, Gang Wang, Xiaoguang Liu, Xiaoxiao Li
Abstract summary: Federated learning (FL) is an emerging machine learning paradigm in which a central server coordinates multiple participants (clients) collaboratively to train on decentralized data. We propose a novel and efficient model-heterogeneous personalized Federated learning framework based on LoRA tuning (pFedLoRA) Experiments on two benchmark datasets demonstrate that pFedLoRA outperforms six state-of-the-art baselines.
Score: 35.59830784463706
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Federated learning (FL) is an emerging machine learning paradigm in which a central server coordinates multiple participants (clients) collaboratively to train on decentralized data. In practice, FL often faces statistical, system, and model heterogeneities, which inspires the field of Model-Heterogeneous Personalized Federated Learning (MHPFL). With the increased interest in adopting large language models (LLMs) in FL, the existing MHPFL methods cannot achieve acceptable computational and communication costs, while maintaining satisfactory model performance. To bridge this gap, we propose a novel and efficient model-heterogeneous personalized Federated learning framework based on LoRA tuning (pFedLoRA). Inspired by the popular LoRA method for fine-tuning pre-trained LLMs with a low-rank model (a.k.a., an adapter), we design a homogeneous small adapter to facilitate federated client's heterogeneous local model training with our proposed iterative training for global-local knowledge exchange. The homogeneous small local adapters are aggregated on the FL server to generate a global adapter. We theoretically prove the convergence of pFedLoRA. Extensive experiments on two benchmark datasets demonstrate that pFedLoRA outperforms six state-of-the-art baselines, beating the best method by 1.35% in test accuracy, 11.81 times computation overhead reduction and 7.41 times communication cost saving.