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Yi Zhou
Yi Zhou
Research Staff Member, IBM Research
Zweryfikowany adres z ibm.com
Tytuł
Cytowane przez
Cytowane przez
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A hybrid approach to privacy-preserving federated learning
S Truex, N Baracaldo, A Anwar, T Steinke, H Ludwig, R Zhang, Y Zhou
Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security …, 2019
8832019
Hybridalpha: An efficient approach for privacy-preserving federated learning
R Xu, N Baracaldo, Y Zhou, A Anwar, H Ludwig
Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security …, 2019
3242019
Tifl: A tier-based federated learning system
Z Chai, A Ali, S Zawad, S Truex, A Anwar, N Baracaldo, Y Zhou, H Ludwig, ...
Proceedings of the 29th International Symposium on High-Performance Parallel …, 2020
2512020
An optimal randomized incremental gradient method
G Lan, Y Zhou
Mathematical programming, 1-49, 2017
2492017
Communication-efficient algorithms for decentralized and stochastic optimization
G Lan, S Lee, Y Zhou
Mathematical Programming, 1-48, 2017
2402017
Conditional gradient sliding for convex optimization
G Lan, Y Zhou
SIAM Journal on Optimization 26 (2), 1379-1409, 2016
1702016
IBM Federated Learning: an Enterprise Framework White Paper V0. 1
H Ludwig, N Baracaldo, G Thomas, Y Zhou, A Anwar, S Rajamoni, Y Ong, ...
arXiv preprint arXiv:2007.10987, 2020
1292020
Towards taming the resource and data heterogeneity in federated learning
Z Chai, H Fayyaz, Z Fayyaz, A Anwar, Y Zhou, N Baracaldo, H Ludwig, ...
2019 USENIX conference on operational machine learning (OpML 19), 19-21, 2019
862019
Mitigating Bias in Federated Learning
A Abay, Y Zhou, N Baracaldo, S Rajamoni, E Chuba, H Ludwig
arXiv preprint arXiv:2012.02447, 2020
792020
Towards federated graph learning for collaborative financial crimes detection
T Suzumura, Y Zhou, N Baracaldo, G Ye, K Houck, R Kawahara, A Anwar, ...
arXiv preprint arXiv:1909.12946, 2019
712019
A unified variance-reduced accelerated gradient method for convex optimization
G Lan, Z Li, Y Zhou
Advances in Neural Information Processing Systems 32, 2019
682019
FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data
R Xu, N Baracaldo, Y Zhou, A Anwar, J Joshi, H Ludwig
Proceedings of the 14th ACM Workshop on Artificial Intelligence and Security …, 2021
632021
Random gradient extrapolation for distributed and stochastic optimization
G Lan, Y Zhou
SIAM Journal on Optimization 28 (4), 2753-2782, 2018
542018
Curse or redemption? how data heterogeneity affects the robustness of federated learning
S Zawad, A Ali, PY Chen, A Anwar, Y Zhou, N Baracaldo, Y Tian, F Yan
Proceedings of the AAAI Conference on Artificial Intelligence 35 (12), 10807 …, 2021
492021
Conditional accelerated lazy stochastic gradient descent
G Lan, S Pokutta, Y Zhou, D Zink
International Conference on Machine Learning, 1965-1974, 2017
422017
FLoRA: Single-shot Hyper-parameter Optimization for Federated Learning
Y Zhou, P Ram, T Salonidis, N Baracaldo, H Samulowitz, H Ludwig
arXiv preprint arXiv:2112.08524, 2021
212021
Adaptive Histogram-Based Gradient Boosted Trees for Federated Learning
YJ Ong, Y Zhou, N Baracaldo, H Ludwig
arXiv preprint arXiv:2012.06670, 2020
192020
LEGATO: A LayerwisE Gradient AggregaTiOn Algorithm for Mitigating Byzantine Attacks in Federated Learning
K Varma, Y Zhou, N Baracaldo, A Anwar
2021 IEEE 14th International Conference on Cloud Computing (CLOUD), 272-277, 2021
172021
Privacy-preserving federated learning
XU Runhua, NB Angel, Y Zhou, A Anwar, HH Ludwig
US Patent App. 16/682,927, 2021
162021
Tiff: Tokenized incentive for federated learning
J Han, AF Khan, S Zawad, A Anwar, NB Angel, Y Zhou, F Yan, AR Butt
2022 IEEE 15th International Conference on Cloud Computing (CLOUD), 407-416, 2022
142022
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