federated learning

Differential Privacy with Federated Learning via Entangled Noise

Federated learning (FL) offers a promising paradigm for training machine learning models on decentralized datasets without directly exchanging the data itself. However, integrating differential privacy (DP) into FL introduces significant accuracy trade-offs. This work proposes a novel approach, "Entangled Noise," to mi...

Privacy-Preserving Federated Learning for Computational Pharmaceutical Applied Aspect in Uganda: Robustness Under Distribution Shift and Sparse Labels

Federated learning offers a pathway toward privacy-preserving computational pharmaceutical analysis across Ugandan health facilities, yet its practical utility is constrained by two under-examined statistical realities: the non-independence of client data distributions and the scarcity of validated labels. We introduce...

Based on Differential Privacy Federated Learning Optimization Algorithm

This paper introduces a novel federated learning optimization algorithm that integrates differential privacy mechanisms to safeguard user data privacy while simultaneously enhancing learning efficiency. Federated learning, while promising for distributed model training, presents significant privacy challenges. Our prop...

Privacy-Preserving Federated Learning for Computational Pharmaceutical Applied Aspect in Uganda: Robustness Under Distribution Shift and Sparse Labels

Federated learning offers a pathway toward privacy-preserving computational pharmaceutical analysis across Ugandan health facilities, yet its practical utility is constrained by two under-examined statistical realities: the non-independence of client data distributions and the scarcity of validated labels. We introduce...

Federated Learning with Blockchain-Based Security Mechanisms

This paper proposes a novel framework for secure federated learning leveraging the inherent properties of blockchain technology. The core claim is that utilizing blockchain's distributed consensus mechanism can establish a robust and reliable system for federated learning, effectively safeguarding the privacy of partic...

Information-Theoretic Foundation for Federated Learning Privacy

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data while preserving data privacy. However, existing privacy-preserving techniques frequently rely on approximations and heuristics, often lacking rigorous mathematical justification. This work presents a novel inf...

Privacy-Preserving Federated Learning for Forced Displacement Broader Than Conflict in Uganda: Robustness Under Distribution Shift and Sparse Labels

Forced displacement in Uganda extends well beyond conflict-driven flight, encompassing disaster-induced mobility, climate pressures and economic relocation, yet machine learning systems designed to support displaced populations typically assume stable data distributions and abundant labelled examples. We introduce a fe...

Decentralized Knowledge Graph Construction via Bayesian Networks and Federated Learning

This paper presents a novel approach to decentralized knowledge graph construction using Bayesian Networks and Federated Learning. The core idea is to distribute the knowledge graph construction process across a network of nodes, each maintaining a local Bayesian Network. Federated Learning is then employed to iterativ...

Algorithmic Fairness via Differential Privacy and Federated Learning

Algorithmic fairness has emerged as a critical concern in the development and deployment of machine learning models, particularly in scenarios where models are trained on diverse and potentially biased datasets. Traditional approaches to achieving fairness often rely on centralized data collection and model training, w...

DP-FedGuard: A Trust-Aware Differential Privacy Framework for Defending Against Label- Flipping Attacks in Federated Learning

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 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...