federated learning

Distributed Knowledge Graph Embedding with Federated Learning for Privacy Preservation

Knowledge graph embedding techniques have gained significant traction in representing complex relationships within knowledge graphs, enabling applications such as link prediction, entity recommendation, and semantic search. However, the training of these embeddings often relies on consolidating vast amounts of data fro...

FedBound a boundary aware optimization strategy for federated medical image segmentation under non IID data

Medical image segmentation is essential for computer-aided diagnosis and treatment planning. Privacy constraints impede centralized training using medical images from diverse healthcare organizations. Federated learning (FL) is developed to facilitate collaborative model training without sharing patient information. Ho...

Decentralized Federated Learning with Differential Privacy via Lagrangian Relaxation

Federated learning (FL) offers a promising paradigm for training machine learning models on decentralized data sources without directly exchanging data. However, achieving robust privacy guarantees alongside high model accuracy remains a significant challenge. This paper introduces a novel approach to decentralized fed...

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...

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...

Digital Twins for Sustainable Groundwater Resources Management: From Monitoring and Prediction to Governance and Resilience—A Review

This article presents a scoping review of the literature on digital twins (DTs) for sustainable groundwater resources management, which constitutes a very recent and rapidly expanding research field, with literature moving quickly from conceptual frameworks to application-oriented systems. The literature, selected thro...

Decentralized Learning with Federated Bayesian Networks

This paper proposes a novel decentralized learning algorithm for Bayesian networks, termed Federated Bayesian Networks (FBNs). The core idea is to enable nodes within a network to learn independently and collaboratively, mirroring the principles of federated learning. Each node maintains its own Bayesian network and up...

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...