FedHPL: Efficient Heterogeneous Federated Learning with Prompt Tuning and Logit Distillation

• URL: http://arxiv.org/abs/2405.17267v1
• Date: Mon, 27 May 2024 15:25:32 GMT
• Title: FedHPL: Efficient Heterogeneous Federated Learning with Prompt Tuning and Logit Distillation
• Authors: Yuting Ma, Lechao Cheng, Yaxiong Wang, Zhun Zhong, Xiaohua Xu, Meng Wang,
• Abstract summary: Federated learning (FL) is a privacy-preserving paradigm that enables distributed clients to collaboratively train models with a central server. We propose FedHPL, a parameter-efficient unified $textbfFed$erated learning framework for $textbfH$eterogeneous settings. We show that our framework outperforms state-of-the-art FL approaches, with less overhead and training rounds.
• Score: 32.305134875959226
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated learning (FL) is a popular privacy-preserving paradigm that enables distributed clients to collaboratively train models with a central server while keeping raw data locally. In practice, distinct model architectures, varying data distributions, and limited resources across local clients inevitably cause model performance degradation and a slowdown in convergence speed. However, existing FL methods can only solve some of the above heterogeneous challenges and have obvious performance limitations. Notably, a unified framework has not yet been explored to overcome these challenges. Accordingly, we propose FedHPL, a parameter-efficient unified $\textbf{Fed}$erated learning framework for $\textbf{H}$eterogeneous settings based on $\textbf{P}$rompt tuning and $\textbf{L}$ogit distillation. Specifically, we employ a local prompt tuning scheme that leverages a few learnable visual prompts to efficiently fine-tune the frozen pre-trained foundation model for downstream tasks, thereby accelerating training and improving model performance under limited local resources and data heterogeneity. Moreover, we design a global logit distillation scheme to handle the model heterogeneity and guide the local training. In detail, we leverage logits to implicitly capture local knowledge and design a weighted knowledge aggregation mechanism to generate global client-specific logits. We provide a theoretical guarantee on the generalization error bound for FedHPL. The experiments on various benchmark datasets under diverse settings of models and data demonstrate that our framework outperforms state-of-the-art FL approaches, with less computation overhead and training rounds.

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