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.

翻译：[translated abstract in Chinese] 联邦学习（FL）无需交换原始数据即可实现协作式入侵检测，但传统FL因全精度梯度传输导致高通信开销，且易遭受梯度推理攻击。本文提出EdgeDetect，一种面向带宽受限的6G-IoT环境、兼顾通信效率与隐私保护的联邦入侵检测系统。EdgeDetect引入梯度智能化机制，即基于中位数的统计二值化方法，将本地更新压缩至$\{+1,-1\}$表示，在保持收敛性的同时将上行负载降低$32$倍。我们进一步对二值化梯度集成Paillier同态加密，在避免暴露个体更新的前提下防御诚实但好奇的服务器。在CIC-IDS2017数据集（280万条流记录，7种攻击类别）上的实验表明：多分类准确率达$98.0\%$，宏F1分数达$97.9\%$，与集中式基线持平；同时将每轮通信量从450~MB降至14~MB（降幅$96.9\%$）。树莓派4部署验证了边缘可行性：内存占用$4.2$~MB、延迟$0.8$~ms、单次推理功耗$12$~mJ，准确率损失低于$0.5\%$。在$5\%$投毒攻击与严重类别失衡条件下，EdgeDetect仍保持$87\%$准确率与$0.95$的少数类F1分数（$p<0.001$），为下一代边缘入侵检测建立了准确率、通信开销与隐私保护的实用权衡方案。