A Novel Algorithm for Personalized Federated Learning: Knowledge Distillation with Weighted Combination Loss

• URL: http://arxiv.org/abs/2504.04642v1
• Date: Sun, 06 Apr 2025 23:22:03 GMT
• Title: A Novel Algorithm for Personalized Federated Learning: Knowledge Distillation with Weighted Combination Loss
• Authors: Hengrui Hu, Anai N. Kothari, Anjishnu Banerjee,
• Abstract summary: pFedKD-WCL integrates knowledge distillation with bi-level optimization to address non-IID challenges.<n>We evaluate pFedKD-WCL on the MNIST dataset and a synthetic dataset with non-IID, using multinomial logistic regression and multilayer perceptron models.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 and multilayer perceptron models. Experimental results demonstrate that pFedKD-WCL outperforms state-of-the-art algorithms, including FedAvg, FedProx, Per-FedAvg, and pFedMe, in terms of accuracy and convergence speed.

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