federated learning

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...

Quantum-Resilient Security Mechanisms for Edge-Based Autonomous Aerial and Vehicular Networks

Autonomous aerial and vehicular systems leverage edge computing and AI for real-time intelligent transportation. However, they face critical security challenges due to distributed architectures and emerging quantum threats. This chapter proposes a quantum-resilient framework integrating post-quantum cryptography with e...

The Dual Use of Generative Artificial Intelligence in Cybersecurity: A Survey from Deepfake Attacks to Intelligent Defense

The rapid iteration of generative Artificial Intelligence (AI) is reshaping the cybersecurity offense-defense landscape. This paper systematically reviews research progress in this field from 2021 to 2026, focusing on the dual use of generative AI in cybersecurity. On the one hand, malicious actors exploit generative A...

IoT-Based Elderly Health Monitoring Information System with Federated Learning for Early Health Risk Prediction

The growing elderly population in Indonesia is directly associated with an increasing burden of non- communicable diseases such as hypertension, heart disease, diabetes mellitus, and chronic obstructive pulmonary disease (COPD), creating a need for continuous rather than periodic health-monitoring mechanisms. This stud...