federated learning

RFLDefense: robust federated learning defense against targeted attacks

Federated learning (FL) is a distributed machine learning framework that protects data privacy by allowing multiple participants to jointly train models without sharing local data. However, the distributed nature of FL systems makes them susceptible to backdoor attacks, where attackers inject malicious updates to disru...

Causality-driven hybrid intrusion detection with entropy-aware feature learning and federated intelligence

Modern IDSs should be capable of detecting and recognizing new forms of attack traffic (including both attacks from known adversaries) and to do so in ways that remain computational feasible when deployed across multiple, geographically distributed sites. Rule based, representation learning, and federated approaches ha...

An adaptive federated deep learning approach for efficient DDoS detection in IoT edge networks

Abstract The rapid growth of the Internet of Things (IoT) has expanded the attack surface of modern networks, motivating the need for accurate detection of DDoS and botnet activity in resource-constrained settings. Centralised intrusion detection systems (IDS) face privacy, scalability, and heterogeneity challenges tha...

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

With machine learning being used more and more in sensitive areas like healthcare and finance, protecting the privacy of training data has become a serious concern. Most existing approaches handle encryption, secure computation, and audit logging as separate steps, which creates gaps where data can be exposed. In this...

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

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