164 citations · 203 across the 5 of their papers we have counts for
6 papers
The Effect of Quantization in Federated Learning: A Rényi Differential Privacy Perspective
Tianqu Kang, Lumin Liu, Hengtao He +3
Federated Learning (FL) is an emerging paradigm that holds great promise for privacy-preserving machine learning using distributed data. To enhance privacy, FL can be combined with…
Binary Federated Learning with Client-Level Differential Privacy
Lumin Liu, Jun Zhang, Shenghui Song +1
Federated learning (FL) is a privacy-preserving collaborative learning framework, and differential privacy can be applied to further enhance its privacy protection. Existing FL sys…
A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency
Jiawei Shao, Zijian Li, Wenqiang Sun +6
Federated learning (FL) has emerged as a secure paradigm for collaborative training among clients. Without data centralization, FL allows clients to share local information in a pr…
Communication-Efficient Federated Distillation with Active Data Sampling
Lumin Liu, Jun Zhang, S. H. Song +1
Federated learning (FL) is a promising paradigm to enable privacy-preserving deep learning from distributed data. Most previous works are based on federated average (FedAvg), which…
Hierarchical Federated Learning with Quantization: Convergence Analysis and System Design
Lumin Liu, Jun Zhang, Shenghui Song +1
Federated learning (FL) is a powerful distributed machine learning framework where a server aggregates models trained by different clients without accessing their private data. Hie…
Client-Edge-Cloud Hierarchical Federated Learning
Lumin Liu, Jun Zhang, S. H. Song +1
Federated Learning is a collaborative machine learning framework to train a deep learning model without accessing clients' private data. Previous works assume one central parameter…