2 papers
cs.LG2024
Towards Fair, Robust and Efficient Client Contribution Evaluation in Federated Learning
Meiying Zhang, Huan Zhao, Sheldon Ebron +1
The performance of clients in Federated Learning (FL) can vary due to various reasons. Assessing the contributions of each client is crucial for client selection and compensation.…
cs.DC2023
Multi-Criteria Client Selection and Scheduling with Fairness Guarantee for Federated Learning Service
Meiying Zhang, Huan Zhao, Sheldon Ebron +2
Federated Learning (FL) enables multiple clients to train machine learning models collaboratively without sharing the raw training data. However, for a given FL task, how to select…