federated learning

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...

Context-Aware Federated Learning with Differential Privacy

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing. However, traditional FL models often operate under the assumption of homogeneous data distributions, which frequently doesn't hold true in real-world scenarios. This researc...

Privacy-Preserving Federated Learning for Conflict Resolution Peacebuilding in Uganda: Robustness Under Distribution Shift and Sparse Labels

Federated learning offers a compelling architecture for peacebuilding analytics in conflict-affected regions, where data sovereignty, institutional sensitivity, and infrastructural fragility preclude centralised data aggregation. We introduce a federated empirical risk minimisation objective regularised by a Wasserstei...

Decentralized Federated Learning with Blockchain-Based Incentive Mechanisms

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, a key challenge in the widespread adoption of FL is the incentive problem: motivating participants to contribute their valuable data and computational resource...

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)...

Privacy-Preserving Federated Learning for Crimes Against Humanity in Uganda: Robustness Under Distribution Shift and Sparse Labels

The documentation and analysis of crimes against humanity in Uganda confront a dual computational challenge: data are distributed across sensitive, institutionally siloed repositories, and the labels required for supervised learning are exceptionally sparse due to the cost and risk of expert annotation. We introduce a...

Construction of a federated learning-based privacy-preserving and intelligent recommendation model for educational data

With the development of the advanced educational platform, more and more personalized recommendation systems are used to increase the efficiency of learning and make the dissemination of the educational resources easier. However, educational recommendation tasks involve highly sensitive student data. The current centra...

Privacy-Preserving Federated Learning for Creative Clusters Districts in Uganda: Robustness Under Distribution Shift and Sparse Labels

Federated learning offers a promising paradigm for training machine learning models across distributed creative industry clusters without centralising sensitive cultural or commercial data. However, its deployment in Ugandan creative districts confronts two interrelated statistical challenges: distribution shift across...