BM-FL: A Balanced Weight Strategy for Multi-Stage Federated Learning Against Multi-Client Data Skewing

Federated Learning (FL) combined with Differential Privacy (DP) is widespread in healthcare, finance, and IoT due to its advantages in multi-client data distribution. However, existing FL approaches overlook the differential impact levels among clients and data redundancy issues, resulting in high computational overhead and limited real-time applicability. Additionally, non-independent identical distribution (Non-IID) and imbalanced datasets in multi-clients pose challenges in privacy preservation and model overfitting. Therefore, we propose a balanced weight strategy for multi-stage federated learning against multi-client data skewing, called BM-FL, which involves clients, intermediate trust servers (ITSs), and the central server (CS). First, to protect data privacy, an improved Laplace
$\epsilon$
-differential privacy method is employed. Second, a novel generative adversarial network (GAN) called BC-GAN is introduced. It is used to generate realistic fake samples and maintain a balanced proportion of samples across different categories. Then, to make full use of each client's valuable data, we designe a balanced weight strategy. Moreover, extensive experimental results clearly demonstrate the effectiveness of BM-FL in efficiently handling classification tasks involving Non-IID and imbalanced datasets while maintaining privacy and security. Furthermore, our method attains superior classification accuracy with fewer training epochs compared to relevant classical algorithms.