| 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 | 1032 | 2019 |
| Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering B Chen, W Carvalho, N Baracaldo, H Ludwig, B Edwards, T Lee, I Molloy, ... arXiv preprint arXiv:1811.03728, 2018 | 846 | 2018 |
| Adversarial Robustness Toolbox v1. 0.0 MI Nicolae, M Sinn, MN Tran, B Buesser, A Rawat, M Wistuba, ... arXiv preprint arXiv:1807.01069, 2018 | 629 | 2018 |
| 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 | 377 | 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 | 305 | 2020 |
| 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 | 159 | 2020 |
| Mitigating Poisoning Attacks on Machine Learning Models: A Data Provenance Based Approach N Baracaldo, B Chen, H Ludwig, JA Safavi Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security …, 2017 | 149 | 2017 |
| Privacy-preserving machine learning: Methods, challenges and directions R Xu, N Baracaldo, J Joshi arXiv preprint arXiv:2108.04417, 2021 | 111 | 2021 |
| Privacy-preserving process mining: Differential privacy for event logs F Mannhardt, A Koschmider, N Baracaldo, M Weidlich, J Michael Business & Information Systems Engineering 61, 595-614, 2019 | 105 | 2019 |
| An Adaptive Risk Management and Access Control Framework to Mitigate Insider Threats N Baracaldo, J Joshi Computers & Security 39, 237-254, 2013 | 105 | 2013 |
| Mitigating Bias in Federated Learning A Abay, Y Zhou, N Baracaldo, S Rajamoni, E Chuba, H Ludwig arXiv preprint arXiv:2012.02447, 2020 | 96 | 2020 |
| 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 | 95 | 2019 |
| Detecting Poisoning Attacks on Machine Learning in IoT Environments N Baracaldo IEEE International Congress on Internet of Things (ICIOT), 2018 | 91 | 2018 |
| 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 | 79 | 2021 |
| Federated Unlearning: How to Efficiently Erase a Client in FL? A Halimi, S Kadhe, A Rawat, N Baracaldo arXiv preprint arXiv:2207.05521, 2022 | 75 | 2022 |
| A trust-and-risk aware RBAC framework: tackling insider threat N Baracaldo, J Joshi Proceedings of the 17th ACM symposium on Access Control Models and …, 2012 | 74 | 2012 |
| 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 | 63 | 2021 |
| Rethinking Machine Unlearning for Large Language Models S Liu, Y Yao, J Jia, S Casper, N Baracaldo, P Hase, X Xu, Y Yao, H Li, ... arXiv preprint arXiv:2402.08787, 2024 | 57 | 2024 |
| User-centered and privacy-driven process mining system design for IoT J Michael, A Koschmider, F Mannhardt, N Baracaldo, B Rumpe Information Systems Engineering in Responsible Information Systems: CAiSE …, 2019 | 47 | 2019 |
| User-centered and privacy-driven process mining system design for IoT J Michael, A Koschmider, F Mannhardt, N Baracaldo, B Rumpe Information Systems Engineering in Responsible Information Systems: CAiSE …, 2019 | 47 | 2019 |
Heiko LudwigResearch Manager, IBM ResearchVerified email at us.ibm.com
