1 citations · 1 across the 3 of their papers we have counts for
5 papers
A Sharded Blockchain-Based Secure Federated Learning Framework for LEO Satellite Networks
Wenbo Wu, Cheng Tan, Kangcheng Yang +3
Low Earth Orbit (LEO) satellite networks are increasingly essential for space-based artificial intelligence (AI) applications. However, as commercial use expands, LEO satellite net…
WindGP: Efficient Graph Partitioning on Heterogenous Machines
Li Zeng, Haohan Huang, Binfan Zheng +6
Graph Partitioning is widely used in many real-world applications such as fraud detection and social network analysis, in order to enable the distributed graph computing on large g…
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.…
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…
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…