An Efficient Federated Learning with Correlation-Based Pruning: Improving Accuracy under Layer-Wise Differential Privacy

Federated Learning (FL) enables multiple clients to collaboratively train models without sharing data. However, it commonly faces dual challenges of security and high communication costs. Differential Privacy (DP) offers protection by adding noise to model parameters based on strict privacy standards, but excessive noise can compromise model accuracy. Additionally, the communication cost associated with training large-scale models in FL can be both slow and expensive. In this paper, we propose CPDP-FL, an efficient and privacy-preserving federated learning algorithm that combines model pruning with differential privacy to address these issues. By pruning the model based on neuron correlation before client training, we reduce redundant parameters, which not only improves communication efficiency but also reduces the amount of noise needed for DP, thereby preserving model accuracy. During training, we apply differential privacy to the remaining parameters and introduce a novel layer-wise privacy budget allocation strategy. This approach assigns different privacy budgets to different layers to balance privacy protection with model accuracy. Extensive experiments demonstrate that our method achieves high communication efficiency and robust privacy protection while minimizing unnecessary privacy budget expenditure.