Byzantine-Robust Federated Fire Detection with a Rotating Coordinator
Updated 41 min ago · first seen 11 Sept 2026
paper_01M294FNQJK66VMBB5N8YS2DWD
- Published
- 11 Sept 2026
- T1 · 41 min ago
- arXiv
- 2609.10647
- T1 · 41 min ago
- Category
- cs.LG
- T1 · 41 min ago
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.
Authors 4
Georgia Argyrou, Aymen Bahrouny, Hedi Fendriy, Alexander Jung
Specification
- Official page
Source:arXiv (Atom API + RSS)T1observed 41 min agohigh
- Arxiv announce type
- new
Source:arXiv (Atom API + RSS)T1observed 41 min agohigh
- arXiv id
- 2609.10647
Source:arXiv (Atom API + RSS)T1observed 41 min agohigh
- Categories
- cs.LG
Source:arXiv (Atom API + RSS)T1observed 41 min agohigh
Source:arXiv (Atom API + RSS)T1observed 41 min agohigh
- Primary category
- cs.LG
Source:arXiv (Atom API + RSS)T1observed 41 min agohigh
- Published
- 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 41 min agohigh
Each value shows its source, tier and observation time. Conflicting claims are kept side by side and flagged — never averaged. How AI Atlas records facts →
Provenance
Attributed facts
9
Source tiers
T19
Freshest observation
41 min ago
Conflicts
None
No models linked to this paper yet.
- Authors
- Georgia Argyrou, Aymen Bahrouny, Hedi Fendriy
As of
Rewind the record: see this entity's attributes exactly as AI Atlas knew them on a given day.
Claim history · Authors
Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →
New paper: Byzantine-Robust Federated Fire Detection with a Rotating Coordinator
arxiv
| Source | Document | Type | Tier | Last observed | Snapshots |
|---|---|---|---|---|---|
| arXiv (Atom API + RSS) | rss.arxiv.org/rss/cs.LG | feed | T1· Official | 41 min ago | 1 |
Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.