198 citations · 482 across the 32 of their papers we have counts for
Showing 2019 · cs.LGShow all
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cs.LG2019★ 40 cited
Privacy for Free: Communication-Efficient Learning with Differential Privacy Using Sketches
Tian Li, Zaoxing Liu, Vyas Sekar +1
Communication and privacy are two critical concerns in distributed learning. Many existing works treat these concerns separately. In this work, we argue that a natural connection e…
cs.LG2019★ 23 cited
Enhancing the Privacy of Federated Learning with Sketching
Zaoxing Liu, Tian Li, Virginia Smith +1
In response to growing concerns about user privacy, federated learning has emerged as a promising tool to train statistical models over networks of devices while keeping data local…
cs.LG2019★ 198 cited
Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions
Zinan Lin, Alankar Jain, Chen Wang +2
Limited data access is a longstanding barrier to data-driven research and development in the networked systems community. In this work, we explore if and how generative adversarial…