activity
20182021
most citedSecure Federated Transfer Learning

631 citations · 2.1k across the 14 of their papers we have counts for

collaborators
Showing 2019Show all

7 papers · 1 filter

cs.LG2019★ 57 cited

A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regression

Kai Yang, Tao Fan, Tianjian Chen +2

Data privacy and security becomes a major concern in building machine learning models from different data providers. Federated learning shows promise by leaving data at providers l…

cs.LG2019★ 58 cited

A Communication Efficient Collaborative Learning Framework for Distributed Features

Yang Liu, Yan Kang, Xinwei Zhang +5

We introduce a collaborative learning framework allowing multiple parties having different sets of attributes about the same user to jointly build models without exposing their raw…

cs.LG2019★ 39 cited

Federated Transfer Reinforcement Learning for Autonomous Driving

Xinle Liang, Yang Liu, Tianjian Chen +2

Reinforcement learning (RL) is widely used in autonomous driving tasks and training RL models typically involves in a multi-step process: pre-training RL models on simulators, uplo…

cs.LG2019★ 112 cited

Abnormal Client Behavior Detection in Federated Learning

Suyi Li, Yong Cheng, Yang Liu +2

In federated learning systems, clients are autonomous in that their behaviors are not fully governed by the server. Consequently, a client may intentionally or unintentionally devi…

eess.SP2019★ 64 cited

HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography

Dashan Gao, Ce Ju, Xiguang Wei +3

Electroencephalography (EEG) classification techniques have been widely studied for human behavior and emotion recognition tasks. But it is still a challenging issue since the data…

cs.AI2019★ 612 cited

Federated Machine Learning: Concept and Applications

Qiang Yang, Yang Liu, Tianjian Chen +1

Today's AI still faces two major challenges. One is that in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and securit…

cs.LG2019★ 177 cited

SecureBoost: A Lossless Federated Learning Framework

Kewei Cheng, Tao Fan, Yilun Jin +4

The protection of user privacy is an important concern in machine learning, as evidenced by the rolling out of the General Data Protection Regulation (GDPR) in the European Union (…