PQFed: A Privacy-Preserving Quality-Controlled Federated Learning Framework
This work addresses data heterogeneity issues in federated learning for applications requiring privacy and personalization, representing an incremental improvement over existing methods.
The paper tackles performance challenges in federated learning due to data heterogeneity by proposing PQFed, a privacy-preserving framework that uses clustering for client selection based on data similarity, resulting in improved model performance on benchmark datasets like CIFAR-10 and MNIST, with better scalability in low-participation scenarios compared to baseline methods.
Federated learning enables collaborative model training without sharing raw data, but data heterogeneity consistently challenges the performance of the global model. Traditional optimization methods often rely on collaborative global model training involving all clients, followed by local adaptation to improve individual performance. In this work, we focus on early-stage quality control and propose PQFed, a novel privacy-preserving personalized federated learning framework that designs customized training strategies for each client prior to the federated training process. PQFed extracts representative features from each client's raw data and applies clustering techniques to estimate inter-client dataset similarity. Based on these similarity estimates, the framework implements a client selection strategy that enables each client to collaborate with others who have compatible data distributions. We evaluate PQFed on two benchmark datasets, CIFAR-10 and MNIST, integrated with three existing federated learning algorithms. Experimental results show that PQFed consistently improves the target client's model performance, even with a limited number of participants. We further benchmark PQFed against a baseline cluster-based algorithm, IFCA, and observe that PQFed also achieves better performance in low-participation scenarios. These findings highlight PQFed's scalability and effectiveness in personalized federated learning settings.