EdgeDetect: Importance-Aware Gradient Compression with Homomorphic Aggregation for Federated Intrusion Detection

TL;DR

EdgeDetect offers a privacy-preserving, communication-efficient federated intrusion detection system suitable for bandwidth-limited 6G-IoT environments, achieving high accuracy with significantly reduced data transmission and robust attack resilience.

Contribution

The paper introduces gradient smartification and homomorphic encryption techniques to enhance federated IDS with low communication overhead and strong privacy guarantees.

Findings

Achieves 98.0% multi-class accuracy on CIC-IDS2017

Reduces communication payload by 96.9% from 450MB to 14MB per round

Maintains high detection performance under poisoning attacks and class imbalance

Abstract

Federated learning (FL) enables collaborative intrusion detection without raw data exchange, but conventional FL incurs high communication overhead from full-precision gradient transmission and remains vulnerable to gradient inference attacks. This paper presents EdgeDetect, a communication-efficient and privacy-aware federated IDS for bandwidth-constrained 6G-IoT environments. EdgeDetect introduces gradient smartification, a median-based statistical binarization that compresses local updates to $\{+1,-1\}$ representations, reducing uplink payload by $32\times$ while preserving convergence. We further integrate Paillier homomorphic encryption over binarized gradients, protecting against honest-but-curious servers without exposing individual updates. Experiments on CIC-IDS2017 (2.8M flows, 7 attack classes) demonstrate $98.0\%$ multi-class accuracy and $97.9\%$ macro F1-score, matching centralized baselines, while reducing per-round communication from $450$~MB to $14$~MB ($96.9\%$ reduction). Raspberry Pi-4 deployment confirms edge feasibility: $4.2$~MB memory, $0.8$~ms latency, and $12$~mJ per inference with $<0.5\%$ accuracy loss. Under $5\%$ poisoning attacks and severe imbalance, EdgeDetect maintains $87\%$ accuracy and $0.95$ minority class F1 ($p<0.001$), establishing a practical accuracy, communication, and privacy tradeoff for next-generation edge intrusion detection.