federated learning

Distributed Learning with Federated Byzantine Fault Tolerance (FBT)

This paper proposes a novel approach to distributed learning that leverages the principles of Federated Byzantine Fault Tolerance (FBT) to achieve robustness and scalability against malicious actors. Traditional federated learning systems are vulnerable to attacks where adversaries can manipulate the global model by in...

Differential Privacy with Federated Learning for Personalized Healthcare

This paper explores the integration of differential privacy (DP) with federated learning (FL) to facilitate personalized healthcare applications while rigorously safeguarding patient privacy. Traditional machine learning approaches relying on centralized data collection pose significant risks to individual confidential...

Differential Privacy for Federated Learning via Homomorphic Encryption

This paper presents a novel approach to enhancing data privacy in federated learning by integrating differential privacy with homomorphic encryption. Federated learning, a distributed machine learning paradigm, allows training models across multiple devices without directly sharing raw data. However, this approach stil...

Privacy-Preserving Federated Learning for Health Informatics Clinical Focus in Uganda: Robustness Under Distribution Shift and Sparse Labels

Federated learning offers a promising architecture for training clinical machine learning models across distributed Ugandan health facilities without centralising sensitive patient data. However, the practical viability of this approach depends on its robustness to two conditions that characterise real-world health inf...

Decentralized Federated Learning via Byzantine-Resilient Graph Protocols

Federated learning (FL) has emerged as a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, the inherent vulnerability of current FL protocols to Byzantine attacks – where malicious nodes inject false information to corrupt the global model –...

Privacy-Preserving Federated Learning for Digital Libraries Quarterly Focus in Uganda: Robustness Under Distribution Shift and Sparse Labels

Federated learning offers a promising architecture for digital library consortia in low-resource settings, where centralising user interaction data is often infeasible due to privacy concerns and bandwidth constraints. We introduce a client-weighted aggregation rule that corrects for covariate shift through importance...

GLA: Structural Regularization via Genetic Optimization of Subspaces for Federated Learning in NLP

Distributed Differential Privacy via Secure Multiparty Computation for Federated Learning

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, this approach inherently exposes sensitive user data, raising significant privacy concerns. This paper proposes a novel framework for distributed differential...

Differential Privacy with Federated Learning for Personalized Healthcare

This paper explores the integration of differential privacy (DP) with federated learning (FL) to facilitate personalized healthcare applications while rigorously safeguarding patient privacy. Traditional machine learning approaches relying on centralized data collection pose significant risks to individual confidential...

Distributed Differential Privacy for Federated Learning

This paper presents a novel approach to integrating differential privacy into federated learning (FL) systems. The core challenge in FL lies in protecting user data while still achieving high model accuracy. This work introduces a distributed differential privacy (DDP) mechanism specifically designed to address this ch...

Privacy-Preserving Federated Learning for Digital Libraries Quarterly Focus in Uganda: Robustness Under Distribution Shift and Sparse Labels

Federated learning offers a promising architecture for digital library consortia in low-resource settings, where centralising user interaction data is often infeasible due to privacy concerns and bandwidth constraints. We introduce a client-weighted aggregation rule that corrects for covariate shift through importance...

Vector Database Dataset

Dataset generated from the following paper. AbstractBackground Strict privacy regulations and institutional barriers have created persistent medical data silos, limiting the development and deployment of artificial intelligence (AI) models across healthcare institutions. Although federated learning provides a potential...