federated learning

Decentralized Federated Learning with Byzantine Fault Tolerance via Differential Privacy

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly sharing the data itself. However, existing FL systems face significant vulnerabilities, including malicious participant attacks and data breaches. This paper proposes a novel decentrali...

Privacy-Preserving Federated Learning for Container Kubernetes Orchestration in Uganda: Robustness Under Distribution Shift and Sparse Labels

Federated learning offers a compelling architecture for training machine learning models across distributed Kubernetes clusters without centralising sensitive data, yet its practical deployment in resource-constrained settings such as Uganda confronts two interrelated obstacles that current literature treats in isolati...

Explainable AI for Federated Learning – Attribution of Contributions

Federated Learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing. However, the inherent lack of transparency in FL raises concerns about trust, fairness, and model reliability. This paper explores the critical need for explainable AI (XAI)...

MHAT-FL: An Open-Source TensorFlow Framework for Federated Human Activity Recognition with Attention-Matrix Positional Encoding

Human Activity Recognition (HAR) systems deployed on wearable and edge devices continuously collect sensitive user data, raising critical privacy concerns and communication bottlenecks. While Federated Learning (FL) mitigates these issues by decentralizing model training and keeping raw data on-device, deploying highly...

Distributed Learning with Personalized Experience Replay for Federated IoT

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources, such as Internet of Things (IoT) devices, without directly exchanging raw data. However, the practical deployment of FL in IoT environments is hindered by several key challenges, including significant...

Adaptive Differential Privacy for Federated Learning via Quantization Noise

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, traditional differential privacy (DP) techniques, designed for centralized learning, often lead to substantial accuracy degradation in FL settings due to the i...

Differential Privacy for Federated Learning via Secure Multi-Party Computation

Federated learning (FL) presents a promising paradigm for training machine learning models on decentralized data sources without directly exchanging the data itself. However, achieving differential privacy (DP) – a rigorous privacy guarantee – within the FL setting remains a significant challenge due to the computation...

Differential Privacy with Adaptive Noise Injection for Federated Learning

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data without directly exchanging the data itself. However, traditional differential privacy (DP) methods often introduce substantial noise into the model updates, significantly degrading model accuracy. This paper p...

Consistency alignment with adaptive aggregation to mitigate model drift in federated learning for the Internet of Medical Things

Distributed Differential Privacy with Dynamic Grouping for Federated Learning

Federated learning (FL) offers a promising approach to train machine learning models on decentralized data sources without directly exchanging the data itself. However, the inherent privacy risks associated with FL remain a significant concern. Traditional differential privacy (DP) techniques often struggle to effectiv...

Differential Privacy with Adaptive Noise Injection for Federated Learning

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data without directly exchanging the data itself. However, traditional differential privacy (DP) methods often introduce substantial noise into the model updates, significantly degrading model accuracy. This paper p...

Distributed Federated Learning with Differential Privacy for Collaborative Graph Analytics

Federated learning (FL) has emerged as a promising paradigm for training machine learning models on decentralized data sources without directly exchanging the raw data. However, the application of FL to graph analytics, particularly when dealing with sensitive graph data, presents significant challenges due to the inhe...