39 citations · 133 across the 28 of their papers we have counts for
5 papers · 1 filter
Moss: Proxy Model-based Full-Weight Aggregation in Federated Learning with Heterogeneous Models
Yifeng Cai, Ziqi Zhang, Ding Li +2
Modern Federated Learning (FL) has become increasingly essential for handling highly heterogeneous mobile devices. Current approaches adopt a partial model aggregation paradigm tha…
DistFL: Distribution-aware Federated Learning for Mobile Scenarios
Bingyan Liu, Yifeng Cai, Ziqi Zhang +5
Federated learning (FL) has emerged as an effective solution to decentralized and privacy-preserving machine learning for mobile clients. While traditional FL has demonstrated its…
PFA: Privacy-preserving Federated Adaptation for Effective Model Personalization
Bingyan Liu, Yao Guo, Xiangqun Chen
Federated learning (FL) has become a prevalent distributed machine learning paradigm with improved privacy. After learning, the resulting federated model should be further personal…
S3ML: A Secure Serving System for Machine Learning Inference
Junming Ma, Chaofan Yu, Aihui Zhou +6
We present S3ML, a secure serving system for machine learning inference in this paper. S3ML runs machine learning models in Intel SGX enclaves to protect users' privacy. S3ML desig…
MOBA-Slice: A Time Slice Based Evaluation Framework of Relative Advantage between Teams in MOBA Games
Lijun Yu, Dawei Zhang, Xiangqun Chen +1
Multiplayer Online Battle Arena (MOBA) is currently one of the most popular genres of digital games around the world. The domain of knowledge contained in these complicated games i…