186 citations · 194 across the 3 of their papers we have counts for
3 papers
cs.CR2022★ 186 cited
The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining
Yi Liu, Lei Xu, Xingliang Yuan +2
In Machine Learning, the emergence of \textit{the right to be forgotten} gave birth to a paradigm named \textit{machine unlearning}, which enables data holders to proactively erase…
cs.CR2022★ 4 cited
Aggregation Service for Federated Learning: An Efficient, Secure, and More Resilient Realization
Yifeng Zheng, Shangqi Lai, Yi Liu +3
Federated learning has recently emerged as a paradigm promising the benefits of harnessing rich data from diverse sources to train high quality models, with the salient features th…
cs.LG2021★ 4 cited
Resource-constrained Federated Edge Learning with Heterogeneous Data: Formulation and Analysis
Yi Liu, Yuanshao Zhu, James J. Q. Yu
Efficient collaboration between collaborative machine learning and wireless communication technology, forming a Federated Edge Learning (FEEL), has spawned a series of next-generat…