81 citations · 136 across the 3 of their papers we have counts for
3 papers
cs.LG2022★ 81 cited
Towards Efficient and Stable K-Asynchronous Federated Learning with Unbounded Stale Gradients on Non-IID Data
Zihao Zhou, Yanan Li, Xuebin Ren +1
Federated learning (FL) is an emerging privacy-preserving paradigm that enables multiple participants collaboratively to train a global model without uploading raw data. Considerin…
cs.LG2019★ 27 cited
Asynchronous Federated Learning with Differential Privacy for Edge Intelligence
Yanan Li, Shusen Yang, Xuebin Ren +1
Federated learning has been showing as a promising approach in paving the last mile of artificial intelligence, due to its great potential of solving the data isolation problem in…
cs.LG2019★ 28 cited
Impact of Prior Knowledge and Data Correlation on Privacy Leakage: A Unified Analysis
Yanan Li, Xuebin Ren, Shusen Yang +1
It has been widely understood that differential privacy (DP) can guarantee rigorous privacy against adversaries with arbitrary prior knowledge. However, recent studies demonstrate…