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;
- (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.