CHFDS: Clustered-based Hierarchical Federated Learning Framework with Differential Privacy and Secure Aggregation

Federated learning is proposed to solve data privacy and security issues for traditional machine learning, which requires the training dataset to be stored locally on a machine or data center for training. However, federated learning may have problems like Non-Independent and Identically Distributed (Non-IID) data and private security. Non-IID can lead to lower training accuracy than expected, and there may be a risk of privacy leakage in the data uploaded by clients. Therefore, this paper proposes CHFDS: Clustered-based Hierarchical Federated Learning Framework with Differential Privacy and Secure Aggregation. Before training begins, we cluster all clients so that the data distribution between clients in each group is similar. This means only a random subset of clients from each cluster is selected in each training round instead of all clients participating in the training. We can use this method to adjust the data balance of participating training. Finally, we add differential privacy and secure aggregation to the clustering and training process to improve the privacy and security of the proposed clustered federated learning framework.