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

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.