federated learning

Blockchain-Based Federated Learning with Differential Privacy

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing. However, traditional FL systems remain vulnerable to privacy breaches and data manipulation. This paper proposes a novel blockchain-based architecture that addresses these c...

DIGITAL TWINS AND FEDERATED LEARNING IN CARDIOMETABOLIC HEALTH: A SYSTEMATIC REVIEW AND A NOVEL PUBLIC HEALTH FRAMEWORK FOR OBESITY, TYPE 2 DIABETES, AND CARDIOVASCULAR DISEASE INTEGRATION

Meta-Learning for Adaptive Model Selection in Federated Learning

Federated Learning (FL) has emerged as a promising approach for training machine learning models across decentralized devices while preserving data privacy. However, a significant limitation of traditional FL is the static nature of model selection. Typically, all clients are trained with the same base model, regardles...

Decentralized Federated Learning with Differential Privacy for Scientific Data

This paper presents a novel approach to collaborative scientific data analysis leveraging Decentralized Federated Learning with Differential Privacy (DFLDP). The core challenge in many scientific domains is the reluctance to share raw data due to stringent privacy regulations and intellectual property protections. Trad...

Decentralized Federated Learning with Byzantine Fault Tolerance using Blockchain-Based Verification

Federated learning (FL) offers a promising approach to training machine learning models on decentralized datasets without directly sharing the data itself. However, existing FL systems are susceptible to Byzantine attacks, where malicious participants can inject poisoned data or manipulate model updates, ultimately com...

A Systematic Literature Review of Emerging Big Data Analytics Using Hadoop: Applications, Trends, and Implications for Sustainability

The rapid expansion of Big Data has transformed data-driven decision-making across diverse sectors, including healthcare, cybersecurity, smart cities, and the Internet of Things (IoT). However, traditional data management platforms struggle to address the increasing volume, velocity, variety, and veracity of modern dat...

Spatiotemporal vision transformers with Byzantine-robust federated prompt tuning for continuous urban perception

While spatial Vision Transformers (ViTs) achieve high precision in urban scene parsing, their frame-by-frame application in autonomous driving suffers from severe temporal flickering and prohibitive retraining costs across decentralized vehicle fleets. To overcome these dual bottlenecks, this paper introduce...

基于自适应参数调整的联邦学习 (Adaptive Parameter Adjustment Federated Learning)

Federated Learning (FL) is a machine learning technique that enables training models on decentralized data sources while preserving data privacy. However, traditional FL approaches often rely on global model updates, which can be vulnerable to adversarial attacks and suboptimal performance on heterogeneous data. This p...

Blockchain-Based Federated Learning with Differential Privacy

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing. However, traditional FL systems remain vulnerable to privacy breaches and data manipulation. This paper proposes a novel blockchain-based architecture that addresses these c...

Hybrid AI-Blockchain Frameworks for Secure Spectrum Data Governance in Smart Grids

The emerging 6G technologies will bring billions of IoT sensors which are going to demand little radio spectrum. The resulting high level of connectivity poses some novel problems, among them network congestion, and privacy: because energy-consumption data might be shared unintentionally, it provides insight into perso...

Federated Learning security with FedEDAuth: Embedding-Distribution Authentication for Counterfeit IC Detection

A Device-Agnostic Framework for Personalized Anomaly Detection in Wearable and Mobile Ecosystems: Applications in Personal Safety and Respiratory Health Surveillance

Abstract—This paper presents a reformulated, device-agnostic architectural framework for two interrelated applications: (i) personalized audio-based personal safety threat detection, and (ii) respiratory pattern anomaly detection for early-stage epidemiological screening. Originating from an ideation dialogue, the prop...