PPBR: Privacy-Preserving and Byzantine-Robust Edge-Assisted Hierarchical Federated Learning in Mobile Networks

Edge-assisted Hierarchical Federated Learning (EHFL) accelerates global model training across mobile devices by hierarchically aggregating models. However, EHFL encounters critical challenges such as privacy risks for local and edge-level models, vulnerability to collusive Byzantine attacks, and issues with model diversity and heterogeneity due to Non-Independent and Identically Distributed (Non-IID) data. In this paper, we propose PPBR, a novel hybrid scheme that subtly integrates Condensed Local Differential Privacy (CLDP) and Packed Linearly Homomorphic Encryption (PLHE) to achieve strong privacy protection and resilience against various Byzantine attacks in Non-IID data scenarios. Specifically, PPBR clusters the sign statistics of local models and clips the norms of edge-level momenta to filter anomalous models and mitigate Byzantine faults while retaining diverse models coming from Non-IID data. To enhance privacy protection with acceptable accuracy loss, the sign tuples of local models are perturbed with CLDP guarantees, and the momenta of edge-level models are encrypted under PLHE. Meanwhile, PPBR enhances privacy in single-edge-server and single-cloud-server aggregations by using random perturbations, secret sharing, and PLHE. In addition to safeguarding privacy with accommodating abrupt dropouts of mobile devices and edge servers, the aggregations effectively mitigate the adverse effects of Non-IID data under advanced Byzantine attacks. Theoretical analysis and comprehensive experiments validate PPBR's strong privacy guarantees and resilience to various Byzantine attacks under Non-IID data.