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

2026-04-16

AI Summary

This work addresses the high communication overhead and vulnerability to gradient inversion attacks inherent in transmitting full-precision gradients in federated intrusion detection. To this end, the authors propose EdgeDetect, a novel framework tailored for 6G-IoT edge environments, which introduces an importance-aware median-statistics-based gradient binarization method. This approach achieves 32× compression while preserving model convergence and, for the first time, integrates Paillier homomorphic encryption with binarized gradients to effectively counter privacy threats from honest-but-curious servers. Experimental results on CIC-IDS2017 demonstrate that EdgeDetect attains 98.0% accuracy and 97.9% macro F1-score, reducing communication volume by 96.9%. Deployed on a Raspberry Pi, it requires only 4.2 MB memory, incurs 0.8 ms latency, and consumes 12 mJ energy, exhibiting strong robustness against adversarial attacks and data imbalance.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Adversarial Attacks & Robustness
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Large-scale security measurements

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.

Problem

• federated learning
• intrusion detection
• gradient compression
• privacy
• communication overhead

Innovation

• gradient binarization
• homomorphic encryption
• federated intrusion detection
• communication-efficient FL
• privacy-preserving edge AI