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

Computer Science > Cryptography and Security [Submitted on 16 Apr 2026] EdgeDetect: Importance-Aware Gradient Compression with Homomorphic Aggregation for Federated Intrusion Detection Noor Islam S. Mohammad 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. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2604.14663 [cs.CR]   (or arXiv:2604.14663v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2604.14663 Focus to learn more Submission history From: Noor Noor S. Mohammad [view email] [v1] Thu, 16 Apr 2026 06:16:14 UTC (1,032 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)