FedFOC: Personalized Federated Learning via Fine-Grained One-Shot Clustering

The rapid growth of industrial Internet of Things (IoT) devices presents significant opportunities for federated learning to advance secure machine learning in industrial IoT systems. However, the performance of global federated learning models deteriorates significantly due to data heterogeneity inherent in industrial IoT systems. To address this issue, we propose a novel personalized federated learning framework via fine-grained one-shot clustering (FedFOC). Our method simultaneously considers the dataset structure and the relative positions of samples to extract compact features for clustering. Specifically, we combine clients’ locally calculated sample-level distances with MinHash signatures of their label sets to transform their data distributions into 1-D vectors to reduce communication costs. These vectors are then clustered to identify the fine-grained similarities between clients’ data distributions. To capture the complex relationships among local data distributions, we perform soft clustering in a one-shot manner and quantify client cluster association strengths. Subsequently, the clustering results are utilized to improve the local models’ personalized performance. Extensive experiments conducted on four widely used public datasets under various heterogeneous scenarios demonstrate that FedFOC outperforms state-of-the-art personalized federated learning methods in the accuracy of local models in almost all scenarios partitioned by Dirichlet. In addition, we also validate the effectiveness of our approach on the industrial dataset, indicating that our method has potential value in real-world applications.