拜占庭鲁棒的联邦火灾检测与轮换协调器
Byzantine-Robust Federated Fire Detection with a Rotating Coordinator
- Aalto University（阿尔托大学）
- Kudelski Labs（库德尔斯基实验室）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对联邦火灾检测中带宽、拜占庭客户端和固定服务器信任问题，提出压缩更新、数据集及结合历史感知聚合与轮换协调器的半去中心化方法，消除单点故障并保持性能。
AI中文摘要：
我们研究了联邦学习（FL）在室内火灾检测中的应用。此类火灾检测系统使用边缘摄像头记录敏感影像，这些影像难以在中央服务器上轻易收集。现有的联邦解决方案留下了三个实际障碍未解决：有限的上行链路带宽、拜占庭（恶意或故障）客户端，以及对单一、永久固定的聚合服务器的无条件信任。我们的主要贡献解决了这三个问题。具体而言，我们提供了（i）一个由八个公共来源汇编的精选室内火灾检测数据集；（ii）一个边缘可部署的检测器，其模型更新被压缩多达10倍，仅带来平衡准确率的小幅损失；以及（iii）一种半去中心化的拜占庭鲁棒联邦学习方法，该方法将历史感知聚合与轮换协调器相结合，驱逐逐轮过滤器遗漏的隐蔽攻击，同时消除固定服务器的单点故障。在保留的测试集上，轮换协调器方法在准确性和检测速度上与固定服务器对应方法相匹配，并且一个物理分布的六节点云部署证实了其可行性。
英文摘要：
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. Our main contributions address all three. In particular, we provide (i) a curated indoor fire-detection dataset assembled from eight public sources; (ii) an edge-deployable detector whose model updates are compressed up to 10 times with only a small loss in balanced accuracy; and (iii) a semi-decentralized Byzantine-robust FL method that combines history-aware aggregation with a rotating coordinator, evicting stealthy attacks that per-round filters miss while removing the fixed-server single point of failure. On the held-out test set the rotating-coordinator method matches its fixed-server counterpart in accuracy and detection speed, and a physically distributed six-node cloud deployment confirms feasibility.
