4 papers
Incentive-Compatible Federated Learning with Stackelberg Game Modeling
Simin Javaherian, Bryce Turney, Li Chen +1
Federated Learning (FL) has gained prominence as a decentralized machine learning paradigm, allowing clients to collaboratively train a global model while preserving data privacy.…
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…
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…