113 citations · 290 across the 6 of their papers we have counts for
4 papers · 1 filter
Continual Local Training for Better Initialization of Federated Models
Xin Yao, Lifeng Sun
Federated learning (FL) refers to the learning paradigm that trains machine learning models directly in the decentralized systems consisting of smart edge devices without transmitt…
Adversarial Feature Alignment: Avoid Catastrophic Forgetting in Incremental Task Lifelong Learning
Xin Yao, Tianchi Huang, Chenglei Wu +2
Human beings are able to master a variety of knowledge and skills with ongoing learning. By contrast, dramatic performance degradation is observed when new tasks are added to an ex…
Federated Learning with Unbiased Gradient Aggregation and Controllable Meta Updating
Xin Yao, Tianchi Huang, Rui-Xiao Zhang +2
Federated learning (FL) aims to train machine learning models in the decentralized system consisting of an enormous amount of smart edge devices. Federated averaging (FedAvg), the…
Federated Learning with Additional Mechanisms on Clients to Reduce Communication Costs
Xin Yao, Tianchi Huang, Chenglei Wu +2
Federated learning (FL) enables on-device training over distributed networks consisting of a massive amount of modern smart devices, such as smartphones and IoT (Internet of Things…