Federated learning (FL) enables collaborative training of a global model while preserving participants' local data privacy, making it ideal for data-sensitive fields like Industrial Internet of Things (IIoT), finance, and healthcare. However, Non-IID data among participants and the presence of malicious participants pose significant challenges to the model's performance and convergence. The global model is difficult to achieve consistent performance across all participants. Therefore, this paper proposes personalized and robust federated learning (PRFL) to handle non-independently and identically distributed (Non-IID) data with malicious participants. First, to enhance the robustness and convergence, a model similarity-based division mechanism is employed. It groups participants with similar data and removes both independent and colluding malicious participants. Second, we propose a three-stage knowledge sharing personalized federated learning framework. Each participant undergoes inner-loop knowledge sharing, outer-loop knowledge sharing, and personalized knowledge distillation, incorporating performance-driven dynamic weighted sharing mechanism. Moreover, extensive experiments demonstrate that PRFL outper forms other advanced personalized federated learning methods across various benchmark datasets, particularly in scenarios with Non-IID data and malicious participants.

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). Firstly, to protect data privacy, an improved Laplace$\epsilon$-differential privacy method is employed. Secondly, 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. The code is available athttps://github.com/ylxzjy/BMFL.git.