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

Hengrui Hu; Anai N. Kothari; Anjishnu Banerjee

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arXiv · 2504.04642

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

Hengrui Hu·Anai N. Kothari·Anjishnu Banerjee

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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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Hengrui Hu, Anai N. Kothari, Anjishnu Banerjee. 2025-04-06. A Novel Algorithm for Personalized Federated Learning: Knowledge Distillation with Weighted Combination Loss. https://arxiv.org/abs/2504.04642

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