12 citations · 26 across the 19 of their papers we have counts for
7 papers · 1 filter
Probabilistic Federated Learning on Uncertain and Heterogeneous Data with Model Personalization
Ratun Rahman, Dinh C. Nguyen
Conventional federated learning (FL) frameworks often suffer from training degradation due to data uncertainty and heterogeneity across local clients. Probabilistic approaches such…
Towards Personalized Quantum Federated Learning for Anomaly Detection
Ratun Rahman, Sina Shaham, Dinh C. Nguyen
Anomaly detection has a significant impact on applications such as video surveillance, medical diagnostics, and industrial monitoring, where anomalies frequently depend on context…
Quantum Federated Learning: A Comprehensive Survey
Dinh C. Nguyen, Md Raihan Uddin, Shaba Shaon +3
Quantum federated learning (QFL) is a combination of distributed quantum computing and federated machine learning, integrating the strengths of both to enable privacy-preserving de…
Federated Learning: A Survey on Privacy-Preserving Collaborative Intelligence
Ratun Rahman
Federated Learning (FL) has emerged as a transformative paradigm in the field of distributed machine learning, enabling multiple clients such as mobile devices, edge nodes, or orga…
Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning
Ratun Rahman, Pablo Moriano, Samee U. Khan +1
Electric load forecasting is essential for power management and stability in smart grids. This is mainly achieved via advanced metering infrastructure, where smart meters (SMs) rec…
Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approach
Ratun Rahman, Neeraj Kumar, Dinh C. Nguyen
Electric load forecasting is essential for power management and stability in smart grids. This is mainly achieved via advanced metering infrastructure, where smart meters (SMs) are…
False Data Injection Attack Detection in Edge-based Smart Metering Networks with Federated Learning
Md Raihan Uddin, Ratun Rahman, Dinh C. Nguyen
Smart metering networks are increasingly susceptible to cyber threats, where false data injection (FDI) appears as a critical attack. Data-driven-based machine learning (ML) method…