federated learning

Convergence, Bias, and Fairness in Federated Learning: A Non-Stationary Multi-Armed Bandit Approach for Heterogeneous Client Selection

In practical federated learning (FL) environments, clients often possess non-IID data, which can degrade model performance and extend convergence times. Effective client selection strategies have emerged as a promising approach to mitigate the challenges posed by statistical heterogeneity across clients. This paper pro...

HFL-Deflect: Defence framework against adaptive coordinated poisoning attacks in hierarchical federated learning

Dataset Integrity, Feature Leakage Discovery, in IoT Intrusion Detection: A Verified, Reproducible Evaluation on IoT-23, ToN-IoT and Edge-IIoTset

Machine-learning-based intrusion detection for the Internet of Things is a rapidly growing literature that is nonetheless frequently undermined by two under-examined threats to validity: unverified dataset provenance and undisclosed feature leakage. This paper proposes XAI-FedFog-HybridNet, a federated intrusion-detect...

Defense-Aware Edge Intelligence Framework for Mitigating Adversarial Attacks in Autonomous UAV and Vehicular Networks

The development of edge intelligence has been essential for autonomous drones and vehicular communications due to their ability to provide fast decision-making and real-time analytics. Nonetheless, edge artificial intelligence is highly susceptible to adversarial attacks that affect sensor inputs, resulting in malfunct...

Federated Automatic Latent Variable Selection in Multi-output Gaussian Processes

This paper explores a federated learning approach that automatically selects the number of latent processes in multi-output Gaussian processes (MGPs). The MGP has seen great success as a transfer learning tool when data is generated from multiple sources/units/entities. A common approach in MGPs to transfer knowledge a...

Personalized Private-Shared Federated Model with an Application to Distributed Additive Manufacturing

Currently, many small and medium-sized organizations struggle with limited data availability and computational resources, leading to poor predictive capabilities due to data isolation. Federated Learning (FL) addresses this by enabling collaborative model training without data sharing, overcoming data silos. However, F...

XPPFEL

Intelligent decision-making for autonomous flying drones and vehicles requires real-time processing capabilities, while at the same time protecting the privacy of collected data, reducing latency, and guaranteeing effective communication. Centralized machine learning frameworks not only leak sensitive data but also res...

Artificial Intelligence in Dementia with Lewy Bodies — Current Applications, Diagnostic Challenges, and Future Perspectives-Video

Dementia with Lewy bodies (DLB) is a clinically and biologically heterogeneous neurodegenerative disorder characterised by cognitive impairment together with variable combinations of cognitive fluctuations, recurrent visual hallucinations, rapid eye movement sleep behaviour disorder, parkinsonism, autonomic dysfunction...

Finite payload bias and delivery correction in wireless federated learning

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Real-time YOLO across Cloud, edge, and IoT: architectures, optimisations, and deployment patterns

Real-time object detection with the YOLO family is now deployed in cloud data centres, edge servers, and tiny IoT devices, each operating under different constraints of latency, bandwidth, memory, energy, and cost. In this paper, a deployment-centric survey of YOLO is presented, treating YOLO as a scalable family of mo...

Anomaly detection of internet of medical things cyberattacks using federated learning

Devices connected to the Internet of Medical Things (IoMT) handle sensitive patient data under strict privacy and resource constraints. Centralized intrusion detection introduces privacy risks and communication bottlenecks, while existing Federated Learning (FL) solutions struggle with class imbalance and data hete...

Internet of things-enabled biomedical engineering: current applications, challenges, and emerging directions

The internet of medical things (IoMT) is a structured integrative review of the internet of things (IoT) that focuses on the medical field, specifically on the integration of physiological sensors, medical devices, communication networks, and the clinical information systems to monitor and track patients' health and co...