Byzantine-Robust Federated Fire Detection with a Rotating Coordinator

• URL: http://arxiv.org/abs/2609.10647v2
• Date: Fri, 11 Sep 2026 07:26:40 GMT
• Title: Byzantine-Robust Federated Fire Detection with a Rotating Coordinator
• Abstract summary: We study the application of federated learning (FL) to indoor fire detection.<n>We provide an edge-deployable detector whose model updates are compressed up to 10 times with only a small loss in balanced accuracy.<n>A semi-decentralized Byzantine-robust FL method that combines history-aware aggregation with a rotating coordinator evicts stealthy attacks that per-round filters miss while removing the fixed-server single point of failure.
• Score: 40.87574349486165
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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 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.

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