federated learning

A Federated Hybrid Deep Learning Framework for Multimodal MRI and CT Brain Tumor Diagnosis with Explainable AI

Practical Byzantine-Resilient Federated Learning via Zero-Knowledge Proofs and Adaptive Reputation

Trust management under adversarial uncertainty is a central challenge in distributed learning systems. We propose Verifiable FL with Two-Stage Selection (VFL-TS), a knowledge-driven framework for Byzantine-resilient federated learning that maintains a dynamic trust knowledge base—updated through cryptographically verif...

Reducing Clinician Annotation Fatigue in Open-Set Federated Learning: A Client-Adaptive Vision-Language Gatekeeper.

PURPOSE Federated learning enables breast-imaging sites to jointly train mammography artificial intelligence (AI) without sharing images, but radiologists at each site must still annotate selected images during active-learning rounds. We developed and evaluated a client-adaptive vision-language gatekeeper that withhold...

PRIVACY-PRESERVING IOT-ENABLED PLANT DISEASE CLASSIFICATION WITH AN ENHANCED FEDERATED LEARNING FRAMEWORK AND EXPLAINABLE ARTIFICIAL INTELLIGENCE

Plant diseases pose a significant threat to global food security by reducing crop yield and quality in large-scale and geographically distributed agricultural systems, where manual inspection is inefficient and error-prone. Recent advances in Internet of Things (IoT) technologies and deep learning have enabled automate...

Byzantine-Robust Federated Learning

Semi-Asynchronous Efficient Hierarchical Federated Learning for heterogeneous resources

A Federated Hybrid Deep Learning Framework for Multimodal MRI and CT Brain Tumor Diagnosis with Explainable AI

DDSS: Federated Fog-Based Microservice Scheduling for IoT-Enabled ICUs

The continuous expansion of connected medical devices in the Intensive Care Unit (ICU) environment has led to the emergence of certain classes of computational problems which cannot be addressed effectively using classical scheduling mechanisms on servers. The static allocation schemes do not work well with the heterog...

Overcoming Memory Bottlenecks in Homomorphic Federated Learning: A Chunk-Based Serverless MapReduce Approach

Federated Learning (FL) enables collaborative model training across decentralized organizations without direct data sharing, yet communicating raw gradient updates remains susceptible to reconstruction and membership inference attacks. Fully Homomorphic Encryption (FHE) provides cryptographic privacy during aggregation...

Federated Learning and Generative AI for Resilient Supply Chain Network Optimization: A Data-Driven Approach

AI-Driven Data Governance Framework for Enterprises in Cloud Environments: Design, Implementation, and Enterprise Evaluation

Challenges cloud-based enterprise data governance is encountering include data growth, regulatory changes, and the inflexibility of traditional rule-based systems. This systematic literature review, based on PRISMA guidelines, analyses 67 peer-reviewed papers (2019–2026) under three research questions related to AI-dri...

Attribution Drift in Federated Intrusion Detection — code and artifacts

Federated learning (FL) trains a shared network-intrusion detector across organisations without pooling raw traffic, and such deployments increasingly demand explainability. Yet FL traffic is deeply **non-IID**, and heterogeneity's effect on a model's *explanations* is uncharacterised. We audit it. Sweeping the Dirichl...