[HTML][HTML] Small models, big impact: A review on the power of lightweight Federated Learning

P Qi, D Chiaro, F Piccialli - Future Generation Computer Systems, 2024 - Elsevier
Abstract Federated Learning (FL) enhances Artificial Intelligence (AI) applications by
enabling individual devices to collaboratively learn shared models without uploading local …

Non-iid data in federated learning: A systematic review with taxonomy, metrics, methods, frameworks and future directions

D Solans, M Heikkila, A Vitaletti, N Kourtellis… - ar** robust artificial intelligence (AI) models that generalize well to unseen datasets
is challenging and usually requires large and variable datasets, preferably from multiple …

FedImpute: Personalized federated learning for data imputation with clusterer and auxiliary classifier

Y Li, S Guo, X Guo, P Zhao, X Ren, H Wang - Expert Systems with …, 2025 - Elsevier
Missing data is a prevalent challenge in real-world applications, hindering the usability and
quality of datasets. Data imputation, a method to substitute missing values, offers a solution …

Mind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI Models

ST Arasteh, C Kuhl, MJ Saehn, P Isfort, D Truhn… - ar** robust artificial intelligence (AI) models that generalize well to unseen datasets
is challenging and usually requires large and variable datasets, preferably from multiple …

Threat Intelligence in IoMTs with Federated Learning using Non-IID Data: An Experimental Analysis

SHA Kazmi, R Hassan, F Qamar… - 2024 IEEE 7th …, 2024 - ieeexplore.ieee.org
In the rapidly evolving field of Artificial Intelligence (AI) empowered cyberspace, securing the
Internet of Medical Things (IoMTs) demands innovative strategies. Intrusion Detection …