6 citations · 6 across the 5 of their papers we have counts for
6 papers
FedACT: Concurrent Federated Intelligence across Heterogeneous Data Sources
Md Sirajul Islam, Isabelle G Chapman, N I Md Ashafuddula +4
Federated Learning (FL) enables collaborative intelligence across decentralized data source devices in a privacy-preserving way. While substantial research attention has been drawn…
SEAFL: Enhancing Efficiency in Semi-Asynchronous Federated Learning through Adaptive Aggregation and Selective Training
Md Sirajul Islam, Sanjeev Panta, Fei Xu +3
Federated Learning (FL) is a promising distributed machine learning framework that allows collaborative learning of a global model across decentralized devices without uploading th…
FedClust: Tackling Data Heterogeneity in Federated Learning through Weight-Driven Client Clustering
Md Sirajul Islam, Simin Javaherian, Fei Xu +3
Federated learning (FL) is an emerging distributed machine learning paradigm that enables collaborative training of machine learning models over decentralized devices without expos…
Skin Cancer Images Classification using Transfer Learning Techniques
Md Sirajul Islam, Sanjeev Panta
Skin cancer is one of the most common and deadliest types of cancer. Early diagnosis of skin cancer at a benign stage is critical to reducing cancer mortality. To detect skin cance…
FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering
Md Sirajul Islam, Simin Javaherian, Fei Xu +3
Federated learning (FL) is an emerging distributed machine learning paradigm enabling collaborative model training on decentralized devices without exposing their local data. A key…
FedFair^3: Unlocking Threefold Fairness in Federated Learning
Simin Javaherian, Sanjeev Panta, Shelby Williams +2
Federated Learning (FL) is an emerging paradigm in machine learning without exposing clients' raw data. In practical scenarios with numerous clients, encouraging fair and efficient…