One-Shot Secure Aggregation: A Hybrid Cryptographic Protocol for Private Federated Learning in IoT

TL;DR

This paper introduces Hyb-Agg, a lightweight, communication-efficient secure aggregation protocol for federated learning in IoT, combining homomorphic encryption and elliptic curve masking to reduce communication rounds and enhance scalability.

Contribution

Hyb-Agg is a novel hybrid cryptographic protocol that achieves single-round, non-interactive secure aggregation suitable for IoT devices, improving scalability and privacy in federated learning.

Findings

Hyb-Agg reduces communication to a single client-to-server message per round.

The protocol maintains strong privacy under standard cryptographic assumptions.

Implementation on Raspberry Pi 4 achieves sub-second execution with 12x communication expansion.

Abstract

Federated Learning (FL) offers a promising approach to collaboratively train machine learning models without centralizing raw data, yet its scalability is often throttled by excessive communication overhead. This challenge is magnified in Internet of Things (IoT) environments, where devices face stringent bandwidth, latency, and energy constraints. Conventional secure aggregation protocols, while essential for protecting model updates, frequently require multiple interaction rounds, large payload sizes, and per-client costs rendering them impractical for many edge deployments. In this work, we present Hyb-Agg, a lightweight and communication-efficient secure aggregation protocol that integrates Multi-Key CKKS (MK-CKKS) homomorphic encryption with Elliptic Curve Diffie-Hellman (ECDH)-based additive masking. Hyb-Agg reduces the secure aggregation process to a single, non-interactive client-to-server transmission per round, ensuring that per-client communication remains constant regardless of the number of participants. This design eliminates partial decryption exchanges, preserves strong privacy under the RLWE, CDH, and random oracle assumptions, and maintains robustness against collusion by the server and up to $N-2$ clients. We implement and evaluate Hyb-Agg on both high-performance and resource-constrained devices, including a Raspberry Pi 4, demonstrating that it delivers sub-second execution times while achieving a constant communication expansion factor of approximately 12x over plaintext size. By directly addressing the communication bottleneck, Hyb-Agg enables scalable, privacy-preserving federated learning that is practical for real-world IoT deployments.