federated learning

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...

AERF: Adaptive Evidence-Assured Predictive-Maintenance Framework

AERF is a modular research framework for governed evaluation of predictive-maintenance models across classification, regression, remaining-useful-life, and physical-edge execution routes. The capsule provides the base framework, campaign executor, model adapters, dataset registry, physical-edge validation utilities, re...

Hyperdimensional Computing for Secure Federated Learning

Federated learning (FL) presents a promising paradigm for training machine learning models across decentralized devices while preserving data privacy. However, the inherent vulnerability of FL to adversarial attacks poses a significant threat to its security and reliability. This paper proposes a novel approach to secu...

Secure and scalable client pre-filtering and authentication framework for federated learning systems

Abstract A federated learning system, which emphasises decentralisation, allows cooperative model training to ensure the privacy and security of the data. Client authentication is an important factor in a decentralised system to prevent unauthorised access and unauthorised users from adding exceptions or irrelevant fac...

Privacy-Preserving Federated Learning for Biophysics Core Life in Uganda: Robustness Under Distribution Shift and Sparse Labels

Laboratories providing biophysics core services in Uganda hold fluorescence images, electrophysiological traces and macromolecular characterisation records that cannot be pooled in a single server for legal, ethical and infrastructural reasons, yet models trained in one site routinely lose accuracy when applied in anot...

Towards secure distributed medical diagnosis: a blockchain-driven MCAU-Net and MAA-Bi-LSTM framework

Lung cancer is considered as the deadliest type of cancer, a good diagnosis as well as effective treatment is based on early detection. By utilizing Deep Learning (DL) techniques, the medical practitioner’s burden is reduced since these models can automate the diagnosis and disease classification. However, these models...