RAFed: Responsive Augmentation and Approximate Update Method for Federated Learning with Non-IID Data

Federated learning is a distributed collaborative training framework that enables multiple clients to share model updates and jointly train deep neural networks without exchanging raw data. Although extensive research has explored data augmentation techniques in federated settings, the naturally non-IID data distributions among clients render blind augmentation prone to severe degradation of the learned model. To solve this problem, we suggest Responsive Augmentation and Approximate Update Method for Fed erated Learning with Non-IID Data (RAFed), aimed at alleviating feature shift in client samples. We leverage a Responsive Augmentation Method to accumulate shared data augmentation policy knowledge through local learning, guiding the policy gradient to consider the impact of data augmentation on unseen local data, and employ an Approximate Update Mechanism to reduce communication costs and achieve efficient policy search. To improve the adaptability of data augmentation policies to local data distributions, we introduce a Dynamic Adaptive Method for searching personalized augmentation policies tailored to heterogeneous clients. Experiments on four popular datasets show that RAFed achieves superior test accuracy and lower communication costs compared to related baselines while providing privacy advantages. The code is available via https://github.com/anonymously123-stcak/RAFed.