66 citations · 68 across the 4 of their papers we have counts for
4 papers
FedCAT: Towards Accurate Federated Learning via Device Concatenation
Ming Hu, Tian Liu, Zhiwei Ling +2
As a promising distributed machine learning paradigm, Federated Learning (FL) enables all the involved devices to train a global model collaboratively without exposing their local…
Towards Fast and Accurate Federated Learning with non-IID Data for Cloud-Based IoT Applications
Tian Liu, Jiahao Ding, Ting Wang +2
As a promising method of central model training on decentralized device data while securing user privacy, Federated Learning (FL)is becoming popular in Internet of Things (IoT) des…
Efficient Federated Learning for AIoT Applications Using Knowledge Distillation
Tian Liu, Zhiwei Ling, Jun Xia +3
As a promising distributed machine learning paradigm, Federated Learning (FL) trains a central model with decentralized data without compromising user privacy, which has made it wi…
FDA3 : Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications
Yunfei Song, Tian Liu, Tongquan Wei +3
Along with the proliferation of Artificial Intelligence (AI) and Internet of Things (IoT) techniques, various kinds of adversarial attacks are increasingly emerging to fool Deep Ne…