# EdgeDetect：面向联邦入侵检测的重要性感知梯度压缩与同态聚合

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-04-16 08:00
- AIHOT 链接：https://aihot.virxact.com/items/cmo6z5esj00mosli5c7xhcy97
- 原文链接：https://arxiv.org/abs/2604.14663

## AI 摘要

EdgeDetect面向6G-IoT带宽受限环境，提出一种通信高效且隐私感知的联邦入侵检测方案。其核心创新"梯度智能化"通过基于中位数的统计二值化将梯度压缩为{+1,-1}表示，使上行负载降低32倍，并集成Paillier同态加密抵御梯度推断攻击。在CIC-IDS2017数据集（280万流，7类攻击）上，系统实现98.0%准确率和97.9%宏F1分数，通信开销从450MB/轮降至14MB（减少96.9%）。树莓派4实测显示单次推理仅需4.2MB内存、0.8ms延迟及12mJ能耗，准确率损失不足0.5%；即便面临5%投毒攻击，仍保持87%准确率与0.95少数类F1值。

## 正文

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 32times 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.