基于轮换协调者的拜占庭鲁棒联邦火灾检测
原标题：Byzantine-Robust Federated Fire Detection with a Rotating Coordinator
AI 摘要
该论文研究联邦学习在室内火灾检测中的应用，针对上行带宽受限、拜占庭客户端攻击以及固定聚合服务器单点故障三个问题提出解决方案。作者构建了来自八个公开来源的室内火灾检测数据集，设计了基于冻结MobileNet-V2特征提取器的边缘可部署检测器，并通过INT8/INT4量化和Top-k稀疏化压缩模型更新。同时提出一种半去中心化拜占庭鲁棒联邦学习方法，结合历史感知聚合与轮换协调者，在保持精度的同时消除固定服务器单点故障，并能在六节点云部署中验证可行性。
正文节选
Byzantine-Robust Federated Fire Detection with a Rotating Coordinator Abstract We study the application of federated learning (FL) to indoor fire detection. Such fire-detection systems use edge cameras that record sensitive footage which cannot easily be collected at a central server. Existing federated solutions leave three practical obstacles unaddressed: limited uplink bandwidth, Byzantine (malicious or faulty) clients, and unconditional trust in a single, permanently fixed aggregation server