ArXiv TLDR

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

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2604.19053

Hung Dang

cs.CRcs.DC

TLDR

CHRONOS is a hardware-assisted framework that decouples cryptographic setup from federated learning training for secure and efficient gradient aggregation in IoT.

Key contributions

  • Decouples cryptographic setup from active FL training using device idle windows.
  • Generates ephemeral keys and PRG keys securely within ARM TrustZone enclaves.
  • Clients mask gradients with stream-cipher; server reconstructs masks if clients drop out.
  • Reduces active-phase aggregation latency by up to 74% and thwarts gradient inversion attacks.

Why it matters

This paper introduces a practical and efficient solution for secure federated learning in IoT. By leveraging hardware enclaves and decoupling crypto, CHRONOS enhances privacy, thwarts attacks, and significantly reduces training latency. It makes secure FL more robust and viable for resource-constrained devices.

Original Abstract

We propose CHRONOS, a hardware-assisted framework that decouples the cryptographic setup required for private gradient aggregation from the active training phase. CHRONOS executes a once-per-epoch server-relayed Diffie-Hellman key exchange during a device's idle window. It generates ephemeral keypairs and derives PRG keys entirely within an ARM TrustZone enclave, ensuring private keys never exist in Normal World memory. Pairwise secrets are sealed in the enclave, and Shamir secret shares of the ephemeral private key are distributed to peers. During training, clients mask gradients with a single stream-cipher evaluation and transmit them in one communication round. A hardware-backed round counter enforces single-use freshness. If clients drop out mid-round, the server reconstructs their masks from peer-held Shamir shares, preserving correct aggregation without repeating the round. Evaluation on Rock Pi 4 devices using OP-TEE demonstrates that CHRONOS achieves OS-level compromise resistance and thwarts state-of-the-art gradient inversion attacks. It reduces active-phase aggregation latency by up to 74% compared to synchronous secure aggregation for 20 clients. The system maintains a persistent Secure World storage footprint of fewer than 700 bytes per device, scaling independently of model dimension.

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