PA-CFL: Privacy-Preserving Adaptive Clustered Federated Learning Under Heterogeneous Data

Source abstract

Clustered federated learning (CFL) addresses data heterogeneity by grouping clients with similar data distributions and training personalized models for each cluster. However, existing CFL schemes depend on clustering-related parameters that require prior knowledge of client data distributions. Such information is often unavailable in federated learning due to privacy constraints and the lack of efficient global coordination. This limitation becomes more severe under highly heterogeneous data distributions. In addition, privacy protection remains a major challenge since existing techniques often introduce substantial computational and communication overhead or significantly degrade model utility. To address these challenges, we propose PA-CFL, a novel privacy-preserving adaptive CFL framework under heterogeneous data. We introduce a novel structure-aware similarity metric to partition users based on their data distributions and decompose the clustering process into pre-clustering and fine clustering phases, thereby eliminating the need for manually specified clustering-related parameters. Furthermore, we propose a lightweight structure-aware privacy-preserving mechanism that decomposes each gradient into important and less important subvectors and applies differentiated protection to them. The less important subvector is protected through local randomization and intra-cluster shuffling with a formal differential privacy guarantee, whereas the important subvector is protected through permutation-based coordinate obfuscation under the non-collusion assumption. Extensive experimental results demonstrate that PA-CFL outperforms state-of-the-art methods in terms of model accuracy and efficiency while maintaining a favorable privacy–utility trade-off.