federated learning

Attribute-Aware Federated Learning with Pseudo-Bag Augmentation for Multi-Center WSI Classification

Fedadasparse: Communication-Efficient Personalized Federated Learning Via Similarity-Based Client Clustering And Adaptive Gradient Sparsification For Non-IID Edge Data

Federated learning (FL) enables collaborative model training across distributed clients without centralizing raw data, but its practical deployment on resource-constrained edge devices is hindered by two persistent challenges: statistical heterogeneity (non-independent and identically distributed, non-IID, client data)...

Personalized and Adaptive Neurocognitive Healthcare Systems

The nature of neurocognitive diseases, including Alzheimer's disease, Parkinson's disease and dementia, is progressive and varied, making it challenging and requiring sophisticated and patient-centered healthcare approaches. In this chapter, we examine adaptive and personalized neurocognitive healthcare systems which i...

EMD-guided adaptive decoupled knowledge distillation for federated meta-learning under data heterogeneity

FedML-DKD is proposed, a robust framework that integrates adaptive Decoupled Knowledge Distillation with federated meta-learning and introduces a heterogeneity-aware weighting mechanism based on Earth Mover’s Distance that helps prevent overfitting to biased local data while improving the reliability of knowledge trans...

Vertical Federated Learning in Medical AI: Concepts, Advances, and Challenges

The growing need for data-driven medical intelligence is constrained by strict privacy regulations and fragmented healthcare data distributed across multiple institutions. Vertical Federated Learning (VFL) offers a secure solution by enabling col-laborative model training on feature-partitioned datasets, where differen...

Cognitive Drift Mapping Using Ambient Micro-Behavioral IoT Signals in Preclinical Neurodegeneration

The chapter introduces the Cognitive Drift Mapping that is a longitudinal and entropy-based approach to detecting preclinical neurodegeneration using ambient micro-behavioral IoT signals. The methodology is the description of gradual changes in daily activities in behavioral patterns such as gait speed, speech response...

FedEdgeR: federated and privacy-preserving edgeR for differential gene expression analysis

FedEdgeR is presented, a federated edgeR implementation protected by secure multi-party computation (SMPC), covering IRLS GLM fitting, three-level dispersion estimation, and the likelihood-ratio test, which consistently outperforms Fisher, Stouffer, random-effects, and RankProd meta-analysis on every tested dataset.

Corrigendum to “LHFL: Logical hierarchical federated learning mechanism in distributed physical AI networks” Internet of Things 39 (2026) 102055

Federated Learning for Resource-Aware Intrusion Detection in IoT Networks: An Empirical Study of IID and Device-Level Non-IID Data

Official reproducibility package and research paper for:"Federated Learning for Resource-Aware Intrusion Detection in IoT Networks: An Empirical Study of IID and Device-Level Non-IID Data". ABSTRACT:The proliferation of Internet of Things (IoT) devices has expanded the network attack surface, increasing the risk of bot...

AI-CARE: A Decision Support System Based On Federated Learning and Continuous Learning

Presentation given at the Virtual Physiological Human Conference in Milan 1-4 September 2026.

FedSwitch: From Standard to Balanced Aggregation via Private Histogram Estimation in Federated Medical Image Classification

Federated learning and autoencoder-based hybrid framework for DDoS detection

Distributed Denial-of-Service (DDoS) attacks can overwhelm network resources and disrupt the availability of cloud services, enterprise systems, and critical infrastructure. Machine-learning-based intrusion detection can help identify malicious traffic from network-flow characteristics. However, conventional centrali...