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Secure aggregation is a cryptographic protocol that securely computes the aggregation of its inputs.
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A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets
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Deep models under the GAN: information leakage from collaborative deep learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security . 603–618
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Membership inference attacks against machine learning models. In 2017 IEEE Symposium on Security and Privacy (SP) . IEEE, 3–18
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Batchcrypt: Efficient homomorphic encryption for cross-silo federated learning. In 2020 { \{ USENIX } \} Annual Technical Conference ( { \{ USENIX } \} { \{ ATC } \} 20) . 493–506
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TensorFlow Federated
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The Skellam Mechanism for Differentially Private Federated Learning. In Advances in Neural Information Processing Systems , M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan (Eds.), Vol. 34. Curran Associates, Inc., 5052–5064
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When the Curious Abandon Honesty: Federated Learning Is Not Private
Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, and Nicolas Papernot. 2021 · 2021
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Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage. 2019 · 2019
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Exploiting unintended feature leakage in collaborative learning. In 2019 IEEE Symposium on Security and Privacy (SP) . IEEE, 691–706
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov. 2019 · 2019
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Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning. In 2019 IEEE Symposium on Security and Privacy (SP) . 739–753
Milad Nasr, Reza Shokri, and Amir Houmansadr. 2019 · 2019
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Language Models are Unsupervised Multitask Learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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A hybrid approach to privacy-preserving federated learning. In Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security . 1–11
Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang, and Yi Zhou. 2019 · 2019
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Verifynet: Secure and verifiable federated learning
Guowen Xu, Hongwei Li, Sen Liu, Kan Yang, and Xiaodong Lin. 2019b · 2019
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Deep Leakage from Gradients. In Advances in Neural Information Processing Systems , H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett (Eds.), Vol. 32. Curran Associates, Inc
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SAFER: Sparse Secure Aggregation for Federated Learning
Constance Beguier and Eric W Tramel. 2020 · 2020
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Lukas Burkhalter, Hidde Lycklama à Nijeholt, Alexander Viand, Nicolas Küchler, and Anwar Hithnawi. 2021 · 2021
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SAFELearn: Secure Aggregation for private FEderated Learning
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Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models
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Scalable Privacy-Preserving Distributed Learning
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Byzantine-robust and privacy-preserving framework for fedml
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Evaluating Gradient Inversion Attacks and Defenses in Federated Learning. In Thirty-Fifth Conference on Neural Information Processing Systems
Yangsibo Huang, Samyak Gupta, Zhao Song, Kai Li, and Sanjeev Arora. 2021 · 2021
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The distributed discrete gaussian mechanism for federated learning with secure aggregation. In International Conference on Machine Learning . PMLR, 5201–5212
Peter Kairouz, Ziyu Liu, and Thomas Steinke. 2021 · 2021
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Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix. In Proceedings of the 38th International Conference on Machine Learning
Maximilian Lam, Gu-Yeon Wei, David Brooks, Vijay Janapa Reddi, and Michael Mitzenmacher. 2021 · 2021
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PPFL: Privacy-Preserving Federated Learning with Trusted Execution Environments. In Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services (Virtual Event, Wisconsin) (MobiSys ’21) . Association for Computing Machinery, New York, NY, USA, 94–108
Fan Mo, Hamed Haddadi, Kleomenis Katevas, Eduard Marin, Diego Perino, and Nicolas Kourtellis. 2021 · 2021
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Federated learning with buffered asynchronous aggregation
John Nguyen, Kshitiz Malik, Hongyuan Zhan, Ashkan Yousefpour, Michael Rabbat, Mani Malek, and Dzmitry Huba. 2021 · 2021
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Secure Aggregation for Buffered Asynchronous Federated Learning
Jinhyun So, Ramy E. Ali, Basak Güler, and Amir Salman Avestimehr. 2021a · 2021
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Turbo-aggregate: Breaking the quadratic aggregation barrier in secure federated learning
Jinhyun So, Başak Güler, and A Salman Avestimehr. 2021b · 2021
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Asynchronous Federated Learning on Heterogeneous Devices: A Survey
Chenhao Xu, Youyang Qu, Yong Xiang, and Longxiang Gao. 2021 · 2021
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See through Gradients: Image Batch Recovery via GradInversion. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE Computer Society, Los Alamitos, CA, USA, 16332–16341
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Cerebro: A platform for multi-party cryptographic collaborative learning. In 30th { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 21)
Wenting Zheng, Ryan Deng, Weikeng Chen, Raluca Ada Popa, Aurojit Panda, and Ion Stoica. 2021 · 2021
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The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning
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Federated Learning with Formal Differential Privacy Guarantees
H. Brendan McMahan and Thakurta Abhradeep. 2022 · 2022
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Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors. In 31st USENIX Security Symposium (USENIX Security 22) . USENIX Association, Boston, MA
Timothy Stevens, Christian Skalka, Christelle Vincent, John Ring, Samuel Clark, and Joseph Near. 2022 · 2022
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