FNCS: Federated Learning Strategy Based on Cosine Similarity under Resource Constraints

Federated learning has been widely applied in healthcare services and real-time object tracking. However, limited by communication resources, such as the server bandwidth, and the impact of client data heterogeneity, the convergence rates and accuracy of federated learning significantly drop. Hence, this study proposes a novel federated normalization learning strategy based on cosine similarity (FNCS). Starting from a new perspective of the relationship between local and global updates of the model, FNCS selects valuable clients to upload updates using cosine similarity. The regularization term is then inserted in the last layer of clients by utilizing cosine distance-based update divergence. Numerous experiments are conducted in PyTorch for accelerated validation. Results show that the high accuracy experiments are conducted on the complex CelebA dataset, and the communication rounds of FNCS are improved by 44.71% and 41.98% compared with FedAvg and FedProx, respectively.