federated learning

Deep reinforcement learning-driven cross-layer optimization of IRS-assisted NOMA with blockchain for secure 6G oriented networks spectrum management

The rapid development of connected devices and data intensive applications is demanding the need for smart spectrum management mechanisms in emerging 6G Networks. Traditional centralized spectrum allocation schemes often find it challenging to cope with excessive wireless environments and larger-scale distributed netwo...

Artificial Intelligence, Foundation Models, and Digital Twins for Climate-Resilient Agriculture: A Review

Climate change, increasing food demand, and resource constraints are accelerating the transition from conventional precision agriculture toward smart, autonomous, and climate-resilient agricultural systems. Artificial intelligence (AI), foundation models, digital twins (DTs), the Internet of Things (IoT), remote sensin...

From Data Fabric to Intelligent Data Mesh: An AI-Enabled Governance Model for Large-Scale Cloud Enterprises

This research explores the transition from traditional data fabric architectures to intelligent data mesh models for large-scale cloud enterprises. It examines how Artificial Intelligence (AI), Machine Learning, automated data discovery, data classification, quality monitoring, and compliance management can support eff...

FLEX: Flexible Federated Learning With Asynchronous Peer-to-Peer Communication and Adaptive Aggregation

In this paper, we introduce FLEX, a flexible federated learning (FL) scheme that combines peer-to-peer communication with asynchronous client updates, enabling clients to operate independently. We further propose a novel aggregation algorithm inspired by gradient-like updates, governed by two key parameters: (i) a simi...

FedMixer: Heterogeneity-Resilient Federated Learning for Time Series Forecasting

Time series forecasting (TSF) models the underlying patterns of historical and current time series data to make predictions for future trends, which is widely applied in varying areas to enhance decision-making, optimize resource allocation, and improve operational efficiency. However, traditional TSF approaches are in...

Federated Unlearning in Agentic Energy Systems: Security and Governance Implications

BlockSecML: A Privacy-Preserving Machine Learning Framework Using Homomorphic Encryption, Advanced Encryption Standard-256 (AES-256), and Blockchain

BlockSecML is presented, a framework that brings together three technologies - Advanced Encryption Standard (AES)-256-Galois/Counter Mode encryption, Paillier partially homomorphic encryption (PHE), and Hyperledger Fabric blockchain - into a single working pipeline for privacy-preserving federated machine learning.

Beyond FedAvg: Comparing Aggregation Strategies for Federated Anomaly Detection in Smart Meter Data

Trustworthy Federated Learning: Calibrating Adversarial Perturbation in Real-Time Health Monitoring

VDFedDiff: a novel versatile defense scheme against adversarial attacks in federated diffusion models

Federated learning (FL) enables multiple clients to collaboratively train machine learning models without sharing their raw data, making it a promising paradigm for privacy-sensitive generative applications. However, diffusion models trained in federated environments remain vulnerable to backdoor, evasion, and model-po...

Differential Privacy-Assisted Lightweight Explainable Multi-Dimensional Learning for Early Gestational Diabetes Prediction in IoMT Applications

Adaptive and Fault-Tolerant Scheduling Framework for Scalable and High-Performance Distributed Systems with Dynamic and Heterogeneous Workloads

High-performance distributed systems (HPDS) are increasingly challenged by dynamic workloads, heterogeneous resources, and susceptibility to faults, which degrade scheduling efficiency and resilience. Traditional heuristic and learning-based scheduling approaches, on the other hand, tend to be poorly adaptable and ha...