Skip to content

Latest commit

 

History

History
34 lines (26 loc) · 5.16 KB

papers.md

File metadata and controls

34 lines (26 loc) · 5.16 KB
Raw

Selected Papers

Overview

  • "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, 2020. https://arxiv.org/abs/2007.10987

Secure and Private Federated Learning

  • "A Hybrid Approach to Privacy-Preserving Federated Learning." (best paper award) Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang. and Yi Zhou, The 12th ACM Workshop on Artificial Intelligence and Security (AISec 2019). An arXiv preprint version can be found at https://arxiv.org/abs/1812.03224
  • "A Syntactic Approach for Privacy-Preserving Federated Learning", O. Choudhury, A. Gkoulalas-Divanis, T. Salonidis, I. Sylla, Y. Park, G. Hsu, A. Das, European Conference on Artificial Intelligence (ECAI), 2020
  • "HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning" Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar and Heiko Ludwig, The 12th ACM Workshop on Artificial Intelligence and Security (AISec 2019). https://arxiv.org/abs/1912.05897
  • "Secure Model Fusion for Distributed Learning Using Partial Homomorphic Encryption", Changchang Liu, Supriyo Chakraborty, Dinesh Verma, in PADG 2019. https://link.springer.com/chapter/10.1007/978-3-030-17277-0_9
  • "Analyzing Federated Learning Through an Adversarial Lens", Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, Seraphin Calo, in ICML 2019. arXiv link: https://arxiv.org/abs/1811.12470
  • "Towards Federated Graph Learning for Collaborative Financial Crimes Detection", Toyotaro Suzumura, Yi Zhou, Nathalie Baracaldo, Guangann Ye, Keith Houck, Ryo Kawahara, Ali Anwar, Lucia Larise Stavarache, Yuji Watanabe, Pablo Loyola, Daniel Klyashtorny, Heiko Ludwig, and Kumar Bhaskaran. NeurIPS 2019 Workshop on Robust AI in Financial Services https://arxiv.org/pdf/1909.12946.pdf
  • "Differential Privacy-enabled Federated Learning for Sensitive Health Data", O. Choudhury, A. Gkoulalas-Divanis, T. Salonidis, I. Sylla, Y. Park, G. Hsu, A. Das, NeurIPS ML4H (Machine Learning for Health), 2019
  • "Predicting Adverse Drug Reactions on Distributed Health Data using Federated Learning", O. Choudhury, Y. Park, T. Salonidis, A. Gkoulalas-Divanis, I. Sylla, A. Das, , American Medical Informatics Association (AMIA), 2019 - Nominated for Distinguished Paper Award.
  • “Model fusion with Kullback–Leibler divergence”, S. Claici, M. Yurochkin, S. Ghosh, and J. Solomon. In International Conference on Machine Learning, 2020.
  • "Sharing Models or Coresets: A Study based on Membership Inference Attack", H. Lu, C. Liu, T. He, S. Wang, K. S. Chan, FL-ICML workshop 2020. arXiv link: https://arxiv.org/abs/2007.02977
  • "Overcoming Noisy and Irrelevant Data in Federated Learning", T. Tuor, S. Wang, B. Jun Ko, C. Liu, K. K. Leung, Accepted version in the 25th International Conference on Pattern Recognition (ICPR). https://arxiv.org/abs/2001.08300

Data Heterogeneity and Resource Constraints

  • "TiFL: A Tier-based Federated Learning System" Zheng Chai, Ahsan Ali, Syed Zawad, Stacey Truex, Ali Anwar, Nathalie Baracaldo, Yi Zhou, Heiko Ludwig, Feng Yan, Yue Cheng 2020 https://arxiv.org/abs/2001.09249
  • "Adaptive Federated Learning in Resource Constrained Edge Computing Systems", Shiqiang Wang, Tiffany Tuor, Theodoros Salonidis, Kin K. Leung, Christian Makaya, Ting He, Kevin Chan, in IEEE Journal on Selected Areas in Communications, 2019. arXiv link: https://arxiv.org/abs/1804.05271
  • "Towards Taming the Resource and Data Heterogeneity in Federated Learning." Chai, Z., Fayyaz, H., Fayyaz, Z., Anwar, A., Zhou, Y., Baracaldo, N., Ludwig, H. and Cheng, Y., 2019. In 2019 {USENIX} Conference on Operational Machine Learning (OpML 19) (pp. 19-21). https://www.usenix.org/system/files/opml19papers-chai.pdf Communication Efficient Federated Learning

Communication Efficient Federated Learning

  • "Bayesian nonparametric federated learning of neural networks". M. Yurochkin, M. Agarwal, S. Ghosh, K. Greenewald, N. Hoang, and Y. Khazaeni. In International Conference on Machine Learning, pages 7252–7261, 2019. https://arxiv.org/abs/1905.12022
  • "Scalable inference of topic evolution via models for latent geometric structures" M. Yurochkin, Z. Fan, A. Guha, P. Koutris, and X. Nguyen. In Advances in Neural Information Processing Systems, 2019. https://arxiv.org/abs/1809.08738
  • "Statistical model aggregation via parameter matching" M. Yurochkin, M. Agarwal, S. Ghosh, K. Greenewald, and N. Hoang. In Advances in Neural Information Processing Systems, 2019. https://arxiv.org/abs/1911.00218
  • "Federated Learning with Matched Averaging" H. Wang, M. Yurochkin, Y. Sun, D. Papailiopoulos, and Y. Khazaeni. In International Conference on Learning Representations, 2020. https://arxiv.org/abs/2002.06440
  • "Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach", P. Han, S. Wang, K. K. Leung, accepted at IEEE ICDCS 2020 - . https://arxiv.org/abs/2001.04756