federated learning

Unlocking Federated Learning for ESG Reporting: Prioritizing Critical Adoption Challenges in an Emerging Economy Context

ABSTRACT The growing focus on environmental, social, and governance (ESG) issues has prompted organizations to explore innovative ways to manage their businesses. It has become imperative for organizations to adopt ESG reporting. The applications of federated learning help to enhance the scalability and credibility of...

FairPFL

FairPFL is a federated learning framework for IoT intrusion detection that treats fairness as an operational requirement: no device population should be left systematically under-protected by the shared detector. It combines a self-correcting fairness controller in the aggregation rule, a split personalization architec...

Trustworthy 6G Networks: Blockchain-Based Federated Learning with Deep Spinal Forward Harmonic Net

Adaptive intelligence in smart microgrids: a systematic review and comparative analysis of RL-based control, communication, and simulation frameworks

Abstract This article is framed as a systematic review with original illustrative benchmarking of reinforcement learning (RL) for smart microgrid (SMG) control, communication, and simulation-to-real deployment. Although prior surveys have reviewed RL for microgrid energy management, they have not jointly analyzed contr...

Artificial Intelligence-Enabled Battery Energy Storage Systems for Renewable Energy: A Comprehensive Review of Technologies, Applications, Challenges, and Future Directions

The rapid growth of renewable energy sources, particularly solar and wind power, has increased the demand for efficient and reliable battery energy storage systems (BESSs) to address intermittency, enhance grid stability, and improve energy management. In recent years, artificial intelligence (AI) has emerged as a tran...

SECURE FEDERATED INTRUSION DETECTION USING HOMOMORPHIC ENCRYPTION: A COMPARATIVE STUDY WITH ENSEMBLE LEARNING

The rapid growth of the Internet of Things (IoT) has also resulted in the increase of the demand in the intrusion detection systems, which can detect suspicious activity and keep the information confidential. The traditional centralized machine learning systems involve attaching the data of the distributed devices to a...

Lithium-ion battery health prediction under exhibit non-independent and identically distributed data: A systematic review of challenges and federated learning solutions

Threshold-grounded decision reliability for predictive maintenance under centralized and federated learning

This capsule supports independent verification of a threshold-grounded predictive-maintenance (PdM) reliability study. The work asks whether maintenance decisions remain defensible under a fixed operating threshold when industrial information is uncertain, class-imbalanced, and split across sites, comparing centralized...

AI in Predictive Healthcare II: A Ten-Domain Structured Evidence Review of Wearable Artificial Intelligence, 2024–2026

This expanded edition re-evaluates and extends a prior structured evidence review of artificial intelligence (AI) in wearable predictive healthcare. Where the original review verified evidence across four domains — atrial fibrillation, continuous glucose monitoring, federated learning and human digital twins — this edi...

A systematic review of AI powered adaptive smart home security using multimodal sensor fusion edge intelligence and privacy preserving architectures

Key findings reveal that current solutions remain fragmented across sensing, fusion, adaptation, and privacy dimensions, and that future research must prioritize unified frameworks jointly addressing multimodal fusion, continual adaptation, edge inference, privacy preservation, and explainability.

FairPFL

FairPFL is a federated learning framework for IoT intrusion detection that treats fairness as an operational requirement: no device population should be left systematically under-protected by the shared detector. It combines a self-correcting fairness controller in the aggregation rule, a split personalization architec...

Machine learning-based risk prediction models for type 2 diabetes in primary care: a scoping review

Evidence on ML-based risk prediction models for T2DM applicable to primary care remains limited despite growing interest in AI for diabetes prediction; greater emphasis is needed on external validation, calibration, prospective implementation, and evaluation across diverse primary care populations before widespread cli...