A Hybrid Encryption Framework for Federated Learning Accelerated on RRAM

Federated learning (FL) enables decentralized collaborative training while preserving data privacy, yet it remains vulnerable to dual privacy threats: client-side training data inference attacks and server-side insecure aggregation. To address these challenges, this study proposes a hybrid encryption framework accelerated on Resistive Random Access Memory (RRAM) that integrates differential privacy (DP) and homomorphic encryption (HE) for multilevel security protection. The framework innovatively harnesses two key characteristics of RRAM devices: (1) The parallel in-memory computing capability enabled by crossbar array architectures reduces the computational complexity of HE-based matrix encryption, achieving real-time encryption throughput; (2) The stochastic cycle-to-cycle read noise inherent to RRAM devices is systematically harnessed as a DP-compliant noise injection mechanism, enabling tunable privacy budgets without additional computational overhead. Experiments demonstrate that the proposed scheme achieves a privacy budget of 1.737 to 5.791 on the CIFAR-10 dataset while maintaining a negligible accuracy loss (1% to 4%). Benchmark results reveal a 270× acceleration over CPU implementations. This framework provides a new hardware implementation path for privacy-preserving machine learning on resource-constrained edge devices.