federated learning

Artificial Intelligence Frameworks in the Prevention and Early Diagnosis of Hepatitis B and C: A Comprehensive Clinical and Computational Review

Despite major medical breakthroughs, Hepatitis B (HBV) and Hepatitis C (HCV) continue to silent-kill millions globally, primarily because they go undetected until severe liver damage sets in. While direct-acting antivirals and suppressive therapies work wonders, our screening methods are lagging behind. We still rely o...

Energy-Aware Clustering and Sleep Scheduling with Federated Learning for Faulty Node Detection in Wireless Sensor Networks

Differentially private federated learning for image analysis in smart-city surveillance

Federated learning combined with differential privacy offers a practical way to train image-analysis models on distributed camera and sensor networks. This combination is most often implemented through DP-SGD, and it avoids centralizing raw visual data. This mini review examines differentially private federated learnin...

The Efficiency-Decentralization-Security Trilemma: A Co-Design Framework for Lightweight, Decentralized AI in Cyber-Physical Systems

Smart systems, the Industrial Internet of Things, and cyber-physical networks increasingly make decisions on the devices where data is generated, on nodes short of memory, compute, energy, and bandwidth, and exposed to real adversaries. Two research currents have grown to meet this: one makes artificial intelligence sm...

Quorum-Bounded Asynchronous Federated Learning under Non-IID Data and Adversarial Clients: A Systems Study of Stale-Update Exclusion and Convergence

Deep Learning for Type 2 Diabetes Prediction: A Benchmarking Study Using the PIMA Indians Diabetes Dataset

This study develops and evaluates a deep neural network (DNN) for T2DM prediction using the PIMA Indian Diabetes dataset and found that glucose, BMI, diabetes pedigree function, and age were the dominant predictors.

Cloud-based solutions for sustainable biomedical data processing and management

The rapid advancement of big data analytics is beginning to significantly impact the progress of medical research and practice, enabling data-driven decision-making and innovation. It has led to the creation of tools for collecting, organizing, processing, and integrating vast amounts of diverse, structured, and unstru...

Multimodal machine learning approaches for heart disease detection and control

Heart disease is one of the major causes of death around the world. This is due to the high and rising number of smokers and other factors. Developing a novel system capable of predicting heart diseases is necessary to control this health hazard. An electrocardiogram (ECG) dataset is utilized to build the machine learn...

Secure Federated Learning Based on the OPTICS Clustering Algorithm and Client Selection

Abstract In Federated Learning (FL), the aggregation servers inevitably face attacks from malicious clients because they cannot directly constrain the behaviour of clients. Effectively detecting and mitigating the impact of these malicious clients on the global model is a critical challenge. This paper proposes a secur...

UAG-MF: Uncertainty-aware generative multimodal fusion for continuous pain estimation in non-verbal patients under clinical occlusions

Official code repository for UAG-MF. Contains training scripts, model implementations, LOSO cross-validation, ablation studies, federated learning simulation, and figure generation code. All results reported in the manuscript are fully reproducible.

Communication-efficient federated learning for edge IoT via combined quantization and sparsification

This paper implements a practical, communication-efficient FL framework that combines update quantization with top-k sparsification with error feedback and evaluates its performance under realistic edge conditions, and provides reproducible MATLAB code and result artifacts.

UAG-MF: Uncertainty-aware generative multimodal fusion for continuous pain estimation in non-verbal patients under clinical occlusions

Official code repository for UAG-MF. Contains training scripts, model implementations, LOSO cross-validation, ablation studies, federated learning simulation, and figure generation code. All results reported in the manuscript are fully reproducible.