Yi Zhou  Yi Zhou photo         

contact information

Research Staff Member
Almaden Research Center, San Jose, CA, USA
  +1dash4089271570

links

Professional Associations

Professional Associations:  INFORMS  |  INFORMS Optimization Society  |  Society for Industrial and Applied Mathematics  |  Society of Women Engineers


2021

Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning
S. Zawad, A. Ali, P. Chen, A. Anwar, Y. Zhou, N. Baracaldo, Y. Tian and F. Yan
The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), 2021


2020

Graph topology invariant gradient and sampling complexity for decentralized and stochastic optimization
G. Lan, Y. Ouyang, and Y. Zhou
arXiv preprint arXiv:2101.00143 , 2020

IBM Federated Learning: an Enterprise Framework White Paper V0. 1
Heiko Ludwig, Nathalie Baracaldo, Gegi Thomas, Yi Zhou, Ali Anwar, Shashank Rajamoni, Yuya Ong, Jayaram Radhakrishnan, Ashish Verma, Mathieu Sinn, Mark Purcell, Ambrish Rawat, Tran Minh, Naoise Holohan, Supriyo Chakraborty, Shalisha Whitherspoon, Dean Steuer, Laura Wynter, Hifaz Hassan, Sean Laguna, Mikhail Yurochkin, Mayank Agarwal, Ebube Chuba, Annie Abay
arXiv:2007.10987 , 2020

Communication-efficient Algorithms for Decentralized and Stochastic Optimization
G. Lan, S. Lee and Y. Zhou
Mathematical Programming 180.1 (2020): 237-284.


2019

A hybrid approach to privacy-preserving federated learning
S. Truex, N. Baracaldo, A. Anwar, T. Steinke, H. Ludwig, R. Zhang, Y. Zhou
InProceedings of the 12th ACM Workshop on Artificial Intelligence and Security, 2019

HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning
R. Xu, N. Baracaldo, Y. Zhou, A. Anwar and H. Ludwig
InProceedings of the 12th ACM Workshop on Artificial Intelligence and Security, 2019

A unified variance-reduced accelerated gradient method for convex optimization
Guanghui Lan, Zhize Li, and Yi Zhou
33rd Conference on Neural Information Processing Systems (NeurIPS 2019)

Towards Taming the Resource and Data Heterogeneity in Federated Learning
Zheng Chai, Hannan Fayyaz, Zeshan Fayyaz, Ali Anwar, Yi Zhou, Nathalie Baracaldo, Heiko Ludwig, Yue Cheng
2019 USENIX Conference on Operational Machine Learning (USENIX OpML'19)


2018

Asynchronous Decentralized Accelerated Stochastic Gradient Descent
Lan, Guanghui and Zhou, Yi
arXiv preprint arXiv:1809.09258, 2018

Random Gradient Extrapolation for Distributed and Stochastic Optimization
Lan, Guanghui and Zhou, Yi
SIAM Journal on Optimization 28(4), 2753--2782, SIAM, 2018

An Optimal Randomized Incremental Gradient Method
Lan, Guanghui and Zhou, Yi
Mathematical programming 171(1-2), 167--215, Springer, 2018


2017

Conditional Accelerated Lazy Stochastic Gradient Descent
Guanghui Lan and Sebastian Pokutta and Yi Zhou and Daniel Zink
Proceedings of the 34th International Conference on Machine Learning, pp. 1965--1974, PMLR, 2017


2016

Conditional Gradient Sliding for Convex Optimization
Lan, Guanghui, and Yi Zhou.
SIAM Journal on Optimization 26(2), 1379--1409, SIAM, 2016