Dynamic Adaptive Collaboration for Personalized Federated Learning

With the rapid development of AI-driven personalized services, model training increasingly depends on highly sensitive user-side data, such as location, social behaviour, and biometric information. These data not only exhibit pronounced non-independent and identically distributed (non-IID) characteristics but also pose serious privacy risks when processed centrally. Achieving efficient personalized modelling while preserving data locality and privacy has thus become a critical challenge in the evolution of personalized AI. In recent years, personalized federated learning (PFL) has gained significant attention for its strong performance in addressing non-IID data challenges. However, existing approaches often fall short in effectively balancing collaborative efficiency with personalization. To overcome this limitation, we propose FedDAC, a dynamically adaptive, collaboration-enhanced personalized federated learning method. By quantitatively assessing the responsiveness of each parameter to non-IID data, FedDAC dynamically selects collaborative clients, ensuring effective cooperation while retaining personalized feature information. Extensive experiments on four benchmark datasets (EMNIST, CIFAR-10, CIFAR-100, and Tiny ImageNet) under two pathological non-IID settings show that FedDAC consistently outperforms strong baselines. It improves accuracy by 1.5–3.2% on average, reaching 5.9% on highly heterogeneous tasks. The source code is publicly available at https://github.com/Lixinqin9/FedDAC-MAIN .