3 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…
cs.LG2023
Identifying the Truth of Global Model: A Generic Solution to Defend Against Byzantine and Backdoor Attacks in Federated Learning (full version)
Sheldon C. Ebron, Meiying Zhang, Kan Yang
Federated Learning (FL) enables multiple parties to train machine learning models collaboratively without sharing the raw training data. However, the federated nature of FL enables…