federated learning

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...

Decentralized Machine Learning for Autonomous Vehicle Control

This paper investigates the application of decentralized machine learning (DML) to autonomous vehicle control. Traditional centralized approaches to autonomous driving often face challenges related to scalability, communication bottlenecks, and vulnerability to single points of failure. This research proposes a novel s...

Decentralized Federated Optimization with Byzantine Fault Tolerance using Blockchain

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging the data itself. However, current FL frameworks are susceptible to attacks from malicious participants, known as Byzantine attacks, which can compromise model integrity and p...

Decentralized Anomaly Detection in Network Traffic using Federated Learning and Differential Privacy

This paper investigates the application of federated learning and differential privacy for decentralized anomaly detection in network traffic. Traditional anomaly detection methods often rely on centralized data collection, posing significant privacy risks and hindering scalability. This research proposes a novel frame...

Quantum Walk Based Federated Learning

Federated learning (FL) offers a promising approach to distributed machine learning by enabling collaborative model training without directly exchanging raw data. However, traditional FL systems are plagued by privacy vulnerabilities and significant communication costs. This paper proposes a novel framework, Quantum Wa...

Decentralized Anomaly Detection in Network Traffic using Federated Learning and Graph Neural Networks

This paper proposes a novel approach to network anomaly detection leveraging the strengths of federated learning and graph neural networks (GNNs). Traditional centralized anomaly detection systems suffer from single points of failure and privacy concerns. Our decentralized system addresses these limitations by enabling...