Federated learning has drawn widespread interest from researchers, yet the data heterogeneity across edge clients remains a key challenge, often degrading model performance. Existing methods enhance model compatibility with data heterogeneity by splitting models and knowledge distillation. However, they neglect the insufficient communication bandwidth and computing power on the client, failing to strike an effective balance between addressing data heterogeneity and accommodating limited client resources. To tackle this limitation, we propose a personalized federated learning method based on cosine sparsification parameter packing and dual-weighted aggregation (FedCSPACK), which effectively leverages the limited client resources and reduces the impact of data heterogeneity on model performance. In FedCSPACK, the client packages model parameters and selects the most contributing parameter packages for sharing based on cosine similarity, effectively reducing bandwidth requirements. The client then generates a mask matrix anchored to the shared parameter package to improve the alignment and aggregation efficiency of sparse updates on the server. Furthermore, directional and distribution distance weights are embedded in the mask to implement a weighted-guided aggregation mechanism, enhancing the robustness and generalization performance of the global model. Extensive experiments across four datasets using ten state-of-the-art methods demonstrate that FedCSPACK effectively improves communication and computational efficiency while maintaining high model accuracy.

Abstract Federated learning is a promising new distributed machine learning paradigm, where the client realizes secure and collaborative multi-user training of machine learning models by retaining private data and sharing model parameters with the server. However, with the frequent interaction of model parameters between the client and the server, the client will consume a large amount of network and arithmetic resources, and resource-constrained clients can hardly maintain model security while ensuring the efficiency of collaborative user training. Therefore, we propose FedRtid, a shuffle differential privacy federated learning scheme with random participation and adaptive time constraints, to improve the efficiency of collaborative user training while considering model privacy. First, in model training, the participating clients have the right to decide on random participation in training locally and independently, to alleviate the user’s resource constraints and reduce the time of user interaction to train the model, while adding differential noise to the shared model parameters to ensure model security. In addition, to avoid the global model security decline of server aggregation due to fewer clients participating in training, and the model accuracy decline caused by adding differential noise to all model parameters, we constructed user sparsification and adaptive time-constrained shuffle techniques to reduce the number of model parameters to which the user adds noise, and enhance the model security. Under two types of data distributions, independently and identically distributed and non-independently and identically distributed, we conduct a large number of experiments on three real datasets, and the results show that FedRtid can effectively balance the accuracy and privacy of the model.