federated learning

Training Without Gathering the Data: A Historical Development Review of Federated Learning and Privacy-Preserving AI

This article presents a narrative review of Federated Learning and Privacy-Preserving AI in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpreti...

HyFair-FL: Fair and Accurate Federated Spectrum Sensing in Cognitive Radio Networks

AI for Optimizing Hardware Resources and Maintenance of Wireless Communication Devices

This study develops an AI-based framework to optimize hardware resource usage and enhance predictive maintenance in wireless communication systems. It addresses key challenges, including noisy sensor data, unreliable anomaly detection, and inefficient maintenance practices, all of which impact reliability, cost, and su...

Artificial Intelligence for Sustainable Wireless Network Management

In today’s world, wireless communication networks are essential for applications in business, entertainment, commerce, health, and safety. Over the years, the technology has advanced from the first generation (1G) to the fifth generation (5G). Several nations are currently implementing 5G, and the next generation of wi...

Intelligent Rehabilitation Systems Based on Big Data Analytics and Artificial Intelligence: A Systematic Review

The world population is aging at an accelerating pace, and the rate of disability caused by chronic diseases and trauma is on the rise. The traditional rehabilitation model has some structural defects, such as subjective evaluation, homogeneity in treatment schemes, imbalance of resource allocation, and insufficient in...

Privacy-Preserving Federated Learning for Building Energy Forecasting: A Differential Privacy Analysis of Aggregation Strategies

This work trains a CNN-LSTM forecasting model under three FL aggregation strategies under three DP-FL aggregation strategies, and advances per-sample gradient clipping in differentially private stochastic gradient descent (DP-SGD), which constrains heterogeneous client updates as the most plausible mechanism.

Mapping machine learning applications across the food value chain through a systematic literature review and bibliometric analysis

Abstract Food industry plays major role in public health, economic and environmental sustainability. Integration of machine learning (ML) is increasingly reshaping elements like safety protocols, quality management and supply chain optimization, though outcomes vary substantially by context and scale. This paper compre...

Event-Triggered Collaborative Control Models for Microgrid Systems

The unique ability to combine distributed renewable energy sources and enhance system reliability, resilience, and system flexibility has made microgrids a key component of modern power system. But the existing time-triggered control approaches involve periodic communication between the distributed controllers irrespec...

FedLC-Trans: privacy-preserving CNN–Transformer fusion for resilient IoMT intrusion detection

The present findings demonstrate reduced centralized raw-data exposure and promising computational characteristics, while edge-device validation and stronger privacy-preserving mechanisms remain important directions for future work.

Equivalent on Accuracy, Ordered in Fidelity: Aggregation Under Partial Reception in Decentralized Federated Learning

Design considerations for in silico drug discovery platform that can accelerate targeted drug repurposing: a focus on rare, neglected, and emerging diseases

INTRODUCTION: Over the past decades, considerable debate has arisen regarding the decline in productivity within the pharmaceutical industry. In this context, drug repurposing has become an increasingly important strategy. Because of its cost- and time-efficient nature, it offers a particularly attractive avenue for id...

Enhancing ZTMAF through federated trust learning, post quantum cryptography integration and seamless cross domain authentication handover

Vehicular Fog Computing (VFC) has a major role to play in facilitating low-latency and real-time communication in Intelligent Transportation Systems (ITS). Nevertheless, there exist major issues in trust management, security, and cross-domain authentication, considering the dynamic nature of vehicular networks. The Zer...