no code implementations • 28 Jan 2021 • Xinle Liang, Yang Liu, Jiahuan Luo, Yuanqin He, Tianjian Chen, Qiang Yang
Federated Learning (FL) provides both model performance and data privacy for machine learning tasks where samples or features are distributed among different parties.
no code implementations • 7 Jul 2020 • Yang Liu, Zhihao Yi, Tianjian Chen
In this paper, we show that even parties with no access to labels can successfully inject backdoor attacks, achieving high accuracy on both main and backdoor tasks.
no code implementations • 15 Jun 2020 • Ce Ju, Ruihui Zhao, Jichao Sun, Xiguang Wei, Bo Zhao, Yang Liu, Hongshan Li, Tianjian Chen, Xinwei Zhang, Dashan Gao, Ben Tan, Han Yu, Chuning He, Yuan Jin
It adopts federated averaging during the model training process, without patient data being taken out of the hospitals during the whole process of model training and forecasting.
no code implementations • 1 Feb 2020 • Suyi Li, Yong Cheng, Wei Wang, Yang Liu, Tianjian Chen
Federated learning systems are vulnerable to attacks from malicious clients.
no code implementations • 23 Jan 2020 • Anbu Huang, YuanYuan Chen, Yang Liu, Tianjian Chen, Qiang Yang
Federated learning is a distributed machine learning framework which enables different parties to collaboratively train a model while protecting data privacy and security.
1 code implementation • 17 Jan 2020 • Yang Liu, Anbu Huang, Yun Luo, He Huang, Youzhi Liu, YuanYuan Chen, Lican Feng, Tianjian Chen, Han Yu, Qiang Yang
Federated learning (FL) is a promising approach to resolve this challenge.
no code implementations • 24 Dec 2019 • Yang Liu, Yan Kang, Xinwei Zhang, Liping Li, Yong Cheng, Tianjian Chen, Mingyi Hong, Qiang Yang
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 data or model parameters.
no code implementations • 1 Dec 2019 • Kai Yang, Tao Fan, Tianjian Chen, Yuanming Shi, Qiang Yang
Our approach can considerably reduce the number of communication rounds with a little additional communication cost per round.
no code implementations • 22 Oct 2019 • Suyi Li, Yong Cheng, Yang Liu, Wei Wang, Tianjian Chen
In federated learning systems, clients are autonomous in that their behaviors are not fully governed by the server.
no code implementations • 14 Oct 2019 • Xinle Liang, Yang Liu, Tianjian Chen, Ming Liu, Qiang Yang
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, uploading the pre-trained model to real-life robots, and fine-tuning the weight parameters on robot vehicles.
1 code implementation • 11 Sep 2019 • Dashan Gao, Ce Ju, Xiguang Wei, Yang Liu, Tianjian Chen, Qiang Yang
To verify the effectiveness of our approach, we conduct experiments on a real-world EEG dataset, consisting of heterogeneous data collected from diverse devices.
1 code implementation • 13 Feb 2019 • Qiang Yang, Yang Liu, Tianjian Chen, Yongxin Tong
We propose a possible solution to these challenges: secure federated learning.
1 code implementation • 25 Jan 2019 • Kewei Cheng, Tao Fan, Yilun Jin, Yang Liu, Tianjian Chen, Dimitrios Papadopoulos, Qiang Yang
This federated learning system allows the learning process to be jointly conducted over multiple parties with common user samples but different feature sets, which corresponds to a vertically partitioned data set.
no code implementations • 8 Dec 2018 • Yang Liu, Yan Kang, Chaoping Xing, Tianjian Chen, Qiang Yang
A secure transfer cross validation approach is also proposed to guard the FTL performance under the federation.