federated learning

FedMetaOpt: a comprehensive survey of metaheuristic optimization for federated learning in client selection, resource allocation, and model aggregation

Edge AI and Federated Learning for Privacy-Preserving IoT Systems: System Architectures, Non-IID Optimization, and Security Bounds

Abstract—The proliferation of Internet of Things (IoT) devices has created an unprecedented surge in decentralized telemetry, sensor data, and localized computational workloads. Conventional cloud-centric machine learning architectures require centralized pooling of raw edge data, which induces prohibitive wide-area ne...

FedQDFU: Non-IID Federated Quantum Learning for Diabetic Foot Ulcer Classification from Deep Image Features

Diabetic foot ulcer (DFU) classification increasingly relies on learning frameworks that exploit medical image representations without centralizing raw data. This study presents FedQDFU, a feature-based federated quantum learning benchmark evaluating a compact variational quantum classifier (VQC) on deep features from...

QLFL: quantum-enabled lightweight federated learning-empowered threat detection model for Internet of Medical Things based healthcare

The integration of Internet of Medical Things (IoMT) has significantly reshaped the landscape of modern healthcare, facilitating seamless communication between connected IoMT devices. Despite these advancements, the growing adoption of healthcare technologies has raised critical cybersecurity and data privacy concerns....

Symbiotect Network: A Federated Artificial Immune System for Advanced AI

This concept paper proposes a Federated Artificial Immune System for advanced AI (FAIIS), an architecture for detecting, containing, learning from and sharing defenses against unauthorized AI behavior after a model begins to cross its intended boundary. The design borrows its structure from the human immune system. San...

Auditable Federated Learning for Multimodal Brain Tumor Classification under Label Skew and Client Dropout

Toward Massive MIMO Receivers: Federated Learning under Adversarial Perturbation

Janus: Dual-Sided Communication and Privacy Optimization for Federated Learning in Autonomous Vehicles

Federated Learning (FL) enables collaborative model training across autonomous vehicles (AVs) without sharing raw driving data, but excessive uplink/downlink communication over bandwidth-constrained vehicular-to-everything links and privacy leakage through gradient inversion attacks limit its deployment. This paper pro...

Noise-Perturbed Federated Learning for Synthetic Wearable Health Monitoring: A Reproducible Multi-Seed Evaluation

Privacy-Preserving Machine Learning Techniques for Cross-Sector Fraud Detection in the South African Financial and Telecommunications Sectors

Cross-sector fraud between South Africa’s financial and telecommunications sectors has intensified, driven by sophisticated criminal networks that exploit operational disparities between these industries. Traditional fraud detection systems, which depend on centralised data sharing and siloed organisational structures,...

Smart Agriculture and Environmental Monitoring: An Edge-AI, IoT, and Federated Learning Framework for Climate-Resilient Farming

This study presents a system architecture, algorithm, and evaluation protocol for smart agriculture and environmental monitoring. It is a method/architecture contribution and does not report field trial results. World food production is still under stress from climate variability, the availability of freshwater resourc...

Delay mitigation under data heterogeneity in decentralized federated learning