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Federated Learning (FL) is an emerging machine learning paradigm that enables multiple clients to jointly train a model to take benefits from diverse datasets from the clients without sharing their local training datasets.
Deep Learning with Differential Privacy. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (CCS)
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SCONE: Secure Linux Containers with Intel SGX. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI)
Sergei Arnautov, Bohdan Trach, Franz Gregor, Thomas Knauth, Andre Martin, Christian Priebe, Joshua Lind, Divya Muthukumaran, Dan O’Keeffe, Mark L. Stillwell, David Goltzsche, Dave Eyers, Rüdiger Kapitza, Peter Pietzuch, and Christof Fetzer. 2016 · 2016
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Intel SGX Explained
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Federated learning: Strategies for improving communication efficiency
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Machine learning with adversaries: Byzantine tolerant gradient descent. In Proceedings of the 31st International Conference on Neural Information Processing Systems . 118–128
Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, and Julien Stainer. 2017 · 2017
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Graphene-SGX: A Practical Library OS for Unmodified Applications on SGX. In USENIX Annual Technical Conference (USENIXATC 17)
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Analyzing federated learning through an adversarial lens. In International Conference on Machine Learning . PMLR
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Tensorscone: A secure tensorflow framework using intel sgx
Roland Kunkel, Do Le Quoc, Franz Gregor, Sergei Arnautov, Pramod Bhatotia, and Christof Fetzer. 2019 · 2019
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SGX-PySpark: Secure Distributed Data Analytics. In Proceedings of the World Wide Web Conference (WWW)
Do Le Quoc, Franz Gregor, Jatinder Singh, and Christof Fetzer. 2019 · 2019
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Local model poisoning attacks to byzantine-robust federated learning. In 29th { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 20)
Minghong Fang, Xiaoyu Cao, Jinyuan Jia, and Neil Gong. 2020 · 2020
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Trust management as a service: Enabling trusted execution in the face of byzantine stakeholders. In 2020 50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)
Franz Gregor, Wojciech Ozga, Sébastien Vaucher, Rafael Pires, Sergei Arnautov, André Martin, Valerio Schiavoni, Pascal Felber, Christof Fetzer, et al · 2020
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TEEMon: A continuous performance monitoring framework for TEEs. In Proceedings of the 21th International Middleware Conference (Middleware)
Robert Krahn, Donald Dragoti, Franz Gregor, Do Le Quoc, Valerio Schiavoni, Pascal Felber, Clenimar Souza, Andrey Brito, and Christof Fetzer. 2020 · 2020
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Provably Secure Federated Learning against Malicious Clients. In Proceedings of the AAAI Conference on Artificial Intelligence
Xiaoyu Cao, Jinyuan Jia, and Neil Zhenqiang Gong. 2021 · 2021
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Microsoft Azure Confidential Computing with Intel SGX
James C Gordon. [n.d.] · 2021
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Data-in-use protection on IBM Cloud using Intel SGX
Pratheek Karnati. 2018 · 2021
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ADAM-CS: Advanced Asynchronous Monotonic Counter Service. In 2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)
André Martin, Cong Lian, Franz Gregor, Robert Krahn, Valerio Schiavoni, Pascal Felber, and Christof Fetzer. 2021 · 2021
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Perun: Secure Multi-Stakeholder Machine Learning Framework with GPU Support
Wojciech Ozga, Do Le Quoc, and Christof Fetzer. 2021b · 2021
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secureTF: A Secure TensorFlow Framework. In Proceedings of the 21th International Middleware Conference (Middleware)
Do Le Quoc, Franz Gregor, Sergei Arnautov, Roland Kunkeland, Pramod Bhatotia, and Christof Fetzer. 2020 · 2020
Cited alongside, same era.
PrivacyFL: A simulator for privacy-preserving and secure federated learning. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (CIKM)
Vaikkunth Mugunthan, Anton Peraire-Bueno, and Lalana Kagal. 2020 · 2020
Cited alongside, same era.
A practical approach for updating an integrity-enforced operating system. In Proceedings of the 21th International Middleware Conference (Middleware)
Wojciech Ozga, Do Le Quoc, and Christof Fetzer. 2020 · 2020
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Enclaves in the Clouds: Legal considerations and broader implications
Jatinder Singh, Jennifer Cobbe, Do Le Quoc, and Zahra Tarkhani. 2020 · 2020
Cited alongside, same era.
Fall of empires: Breaking Byzantine-tolerant SGD by inner product manipulation. In Uncertainty in Artificial Intelligence . PMLR
Cong Xie, Oluwasanmi Koyejo, and Indranil Gupta. 2020 · 2020
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Avocado: A Secure In-Memory Distributed Storage System. In 2021 { \{ USENIX } \} Annual Technical Conference ( { \{ USENIX } \} { \{ ATC } \} 21)
Maurice Bailleu, Dimitra Giantsidi, Vasilis Gavrielatos, Do Le Quoc, Vijay Nagarajan, and Pramod Bhatotia. 2021 · 2021
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Perun: Confidential Multi-stakeholder Machine Learning Framework with Hardware Acceleration Support. In IFIP Annual Conference on Data and Applications Security and Privacy . Springer, 189–208
Wojciech Ozga, Christof Fetzer, et al
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Wojciech Ozga, Do Le Quoc, and Christof Fetzer. 2021c · 2021
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Secure Federate Learning using SCONE and Intel-OpenFL
Do Le Quoc. 2021 · 2021
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OpenFL: An open-source framework for Federated Learning
G Anthony Reina, Alexey Gruzdev, Patrick Foley, Olga Perepelkina, Mansi Sharma, Igor Davidyuk, Ilya Trushkin, Maksim Radionov, Aleksandr Mokrov, Dmitry Agapov, Jason Martin, Brandon Edwards, Micah J. Sheller, Sarthak Pati, Prakash Narayana Moorthy, Shih han Wang, Prashant Shah, and Spyridon Bakas. 2021 · 2021
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Enclaves in the Clouds
Jatinder Singh, Jennifer Cobbe, Do Le Quoc, and Zahra Tarkhani. 2021 · 2021
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CAS policy language v0.3
Scontain Team. [n.d.] · 2021
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