The paper introduces a federated learning approach for indoor fire detection that tackles three practical challenges: limited uplink bandwidth, Byzantine clients, and reliance on a single fixed aggregation server. It presents a curated dataset from eight public sources, an edge‑deployable detector with up to 10× compression of model updates, and a semi‑decentralized Byzantine‑robust FL method using a rotating coordinator to mitigate stealthy attacks and eliminate single points of failure. Experiments show that the rotating‑coordinator method matches the accuracy and detection speed of a fixed‑server counterpart and is feasible in a physically distributed six‑node cloud deployment.
arXiv:2409. 17754v2 Announce Type: replace-cross Abstract: Federated Learning (FL) emerges as a distributed machine learning approach that addresses privacy concerns by training AI models locally on devices.
arXiv:2608. 01095v1 Announce Type: new Abstract: Federated learning (FL) enables multiple intelligent devices to collaboratively train a high-accuracy model without sharing raw data.
arXiv:2606. 27622v1 Announce Type: new Abstract: Byzantine-robust federated learning seeks to protect distributed model training from malicious or corrupted clients without requiring access to their private data.
arXiv:2607. 02636v1 Announce Type: cross Abstract: Object detection is a fundamental capability for AI-driven perception in safety-critical drone and edge-vision systems, including disaster response, operational security environments, infrastructure monitoring and defense applications.
arXiv:2609.15521v1 Announce Type: new Abstract: Federated learning enables multiple parties to train a shared model without centralizing raw data with the help of an aggregator, but introduces integr...
The paper proposes Byrd-NAFL, a Byzantine‑robust federated learning algorithm that incorporates Nesterov’s momentum and resilient aggregation rules. It achieves fast and safe convergence under non‑convex, smooth loss functions with relaxed gradient assumptions, and provides a finite‑time convergence guarantee. Experiments show that Byrd-NAFL outperforms existing methods in convergence speed, accuracy, and resilience to various malicious attacks.
arXiv:2608.21137v1 Announce Type: new Abstract: Decentralized Federated Learning (DFL) promises trust-free collaborative learning by replacing the centralized parameter server with peer-to-peer model...
The paper investigates the effects of label‑flipping poisoning attacks in a three‑client federated intrusion detection system (IDS) trained on CICIDS2017 with non‑IID attack subtype distributions. Flipping 60% of training labels from a single Byzantine client reduces the attacker’s own detection accuracy from 99.96% to 84.33%, while the federated global ensemble remains stable across all tested poison rates. The study shows that the self‑compromise signal can be detected as an anomaly, enabling Byzantine client identification without target data exfiltration, and notes that the current aggregation uses a Federated Forest rather than FedAvg, with future work planned to extend to parametric classifiers.
arXiv:2509. 11974v2 Announce Type: replace-cross Abstract: Federated Learning (FL) enables collaborative model training among clients without centralising data, making it a widely adopted privacy-enhancing technology (PET).
The paper introduces Progressive-Resolution Secure Aggregation (PSA) for federated learning, enabling clients to upload updates once while allowing progressively finer aggregate resolutions to be authorized later without further client participation. PSA represents each clipped, dithered update using nested-lattice digits, with each layer protected by secure aggregation and an additional aggregate pad that is released only upon authorization by a non‑colluding release controller.
DecoyTrace is a proactive cyber‑deception defense designed for strictly serverless decentralized federated learning (DFL). It deploys mobile DecoyNodes that generate chaotic decoy challenges, disseminate dual models (clean vs. decoy) based on neighbor trust, and use three‑state semantic metrics to isolate malicious sources and recover models. Across sixty configurations on the NEBULA platform, DecoyTrace restores model utility with minimal performance loss and reduces CPU and network usage by up to two‑thirds.
arXiv:2608. 06447v1 Announce Type: cross Abstract: In modern distributed network environments, particularly in Internet of Things infrastructures and 5G networks, stringent privacy preservation and scalability requirements have created significant challenges for intrusion detection systems.