CHRONOS: A Hardware-Assisted Phase-Decoupled Framework for Secure Federated Learning in IoT

Abstract

Federated learning enables collaborative training on IoT gateways without sharing raw data, yet gradients remain susceptible to inversion attacks. Existing Secure Multiparty Computation defenses impose prohibitive communication overhead, exceeding strict IoT latency and energy budgets. We propose CHRONOS, a hardware-assisted framework that decouples cryptographic setup from active training. During idle windows, CHRONOS executes a once-per-epoch server-relayed Diffie-Hellman exchange within an ARM TrustZone enclave, sealing shared secrets and distributing Shamir shares to peers. During training, clients mask gradients via a single stream-cipher evaluation and transmit in one round; a hardware-backed counter enforces mask freshness. If clients drop mid-round, the server reconstructs masks from peer-held shares (k x 32 bytes/client), preserving aggregation without round repetition. Evaluation on a 32-node heterogeneous testbed (Rock Pi 4 and Orange Pi 5) shows that CHRONOS reduces active-phase latency by up to 74\% over synchronous secure aggregation. It mitigates gradient inversion while maintaining a persistent Secure World footprint under 1.1 KB, independent of model dimension and training horizon.