Federated learning (FL) offers a privacy-preserving framework for distributed machine learning, enabling collaborative model training across diverse clients without centralizing sensitive data. However, statistical heterogeneity, characterized by non-independent and identically distributed (non-IID) client data, poses significant challenges, leading to model drift and poor generalization. This paper proposes a novel algorithm, pFedKD-WCL (Personalized Federated Knowledge Distillation with Weighted Combination Loss), which integrates knowledge distillation with bi-level optimization to address non-IID challenges. pFedKD-WCL leverages the current global model as a teacher to guide local models, optimizing both global convergence and local personalization efficiently. We evaluate pFedKD-WCL on the MNIST dataset and a synthetic dataset with non-IID partitioning, using multinomial logistic regression (MLR) and multilayer perceptron models (MLP). Experimental results demonstrate that pFedKD-WCL outperforms state-of-the-art algorithms, including FedAvg, FedProx, PerFedAvg, pFedMe, and FedGKD in terms of accuracy and convergence speed. For example, on MNIST data with an extreme non-IID setting, pFedKD-WCL achieves accuracy improvements of 3.1 % , 3.2 % , 3.9 % , 3.3 % , and 0.3 % for an MLP model with 50 clients compared to FedAvg, FedProx, PerFedAvg, pFedMe, and FedGKD, respectively, while gains reach 24.1 % , 22.6 % , 2.8 % , 3.4 % , and 25.3 % for an MLR model with 50 clients. [ABSTRACT FROM AUTHOR]

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