FedAFR: Enhancing Federated Learning with adaptive feature reconstruction
Youxin Huang, Shunzhi Zhu, Weizhe Chen, Zhicai Huang · Computer Communications · 2023
Federated learning is a distributed machine learning method where clients train models on local data to ensure that data will not be transmitted to a central server, providing unique advantages in privacy protection. However, in real-world scenarios, data between different clients may be non-Independently and Identically Distributed (non-IID) and imbalanced, leading to discrepancies among local models and impacting the efficacy of global model aggregation. To tackle this issue, this paper proposes a novel framework, FedARF, designed to improve Federated Learning performance by adaptively reconstructing local features during training. FedARF offers a simple reconstruction module for aligning feature representations from various clients, thereby enhancing the generalization capability of cross-client aggregated models. Additionally, to better adapt the model to each client’s data distribution , FedARF employs an adaptive feature fusion strategy for a more effective blending of global and local model information, augmenting the model’s accuracy and generalization performance . Experimental results demonstrate that our proposed Federated Learning method significantly outperforms existing methods in variety image classification tasks, achieving faster model convergence and superior performance when dealing with non-IID data distributions.