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. 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 time 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.

Machine Learning (cs.LG)

I.2.11; I.4.8; C.2.4

arXiv:2609.10647 [cs.LG]

(or arXiv:2609.10647v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.10647 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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