| 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 | 1178 | 2019 |
| 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 | 424 | 2019 |
| 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 | 349 | 2020 |
| An optimal randomized incremental gradient method G Lan, Y Zhou Mathematical programming, 1-49, 2017 | 267 | 2017 |
| Communication-efficient algorithms for decentralized and stochastic optimization G Lan, S Lee, Y Zhou Mathematical Programming, 1-48, 2017 | 265 | 2017 |
| 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 | 185 | 2020 |
| Conditional gradient sliding for convex optimization G Lan, Y Zhou SIAM Journal on Optimization 26 (2), 1379-1409, 2016 | 185 | 2016 |
| Mitigating Bias in Federated Learning A Abay, Y Zhou, N Baracaldo, S Rajamoni, E Chuba, H Ludwig arXiv preprint arXiv:2012.02447, 2020 | 118 | 2020 |
| 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 | 99 | 2019 |
| 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 | 99 | 2019 |
| 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 | 94 | 2021 |
| A unified variance-reduced accelerated gradient method for convex optimization G Lan, Z Li, Y Zhou Advances in Neural Information Processing Systems 32, 2019 | 74 | 2019 |
| 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 | 70 | 2021 |
| Random gradient extrapolation for distributed and stochastic optimization G Lan, Y Zhou SIAM Journal on Optimization 28 (4), 2753-2782, 2018 | 61 | 2018 |
| Granite Code Models: A Family of Open Foundation Models for Code Intelligence M Mishra, M Stallone, G Zhang, Y Shen, A Prasad, AM Soria, M Merler, ... arXiv preprint arXiv:2405.04324, 2024 | 45 | 2024 |
| Conditional accelerated lazy stochastic gradient descent G Lan, S Pokutta, Y Zhou, D Zink International Conference on Machine Learning, 1965-1974, 2017 | 45 | 2017 |
| Privacy-preserving federated learning XU Runhua, NB Angel, Y Zhou, A Anwar, HH Ludwig US Patent App. 16/682,927, 2021 | 44 | 2021 |
| Adaptive Histogram-Based Gradient Boosted Trees for Federated Learning YJ Ong, Y Zhou, N Baracaldo, H Ludwig arXiv preprint arXiv:2012.06670, 2020 | 29 | 2020 |
| Less-vfl: Communication-efficient feature selection for vertical federated learning T Castiglia, Y Zhou, S Wang, S Kadhe, N Baracaldo, S Patterson International Conference on Machine Learning, 3757-3781, 2023 | 27 | 2023 |
| 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 | 27 | 2021 |
Heiko LudwigResearch Manager, IBM ResearchDirección de correo verificada de us.ibm.com
