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Federated Learning (FL) enables collaborative training among mutually distrusting parties.
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Inverting Gradients - How easy is it to break Privacy in Federated Learning?
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Byzantine-robust distributed learning: Towards optimal statistical rates. In International Conference on Machine Learning . PMLR, 5650–5659
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Advances and open problems in federated learning
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Peer-to-peer federated learning on graphs
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Exploiting unprotected I/O operations in AMD’s Secure Encrypted Virtualization. In 28th USENIX Security Symposium . USENIX, 1257–1272
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CROSSLINE: Breaking ”Security-by-Crash” based Memory Isolation in AMD SEV
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IBM Federated Learning: an Enterprise Framework White Paper V0.1
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Strengthening VM isolation with integrity protection and more
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LVI: Hijacking Transient Execution through Microarchitectural Load Value Injection. In 2020 IEEE Symposium on Security and Privacy . IEEE, 54–72
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CacheOut: Leaking data on Intel CPUs via cache evictions
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Luca Wilke, Jan Wichelmann, Mathias Morbitzer, and Thomas Eisenbarth. 2020 · 2020
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iDLG: Improved Deep Leakage from Gradients
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen. 2020 · 2020
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A high performance, open source universal RPC framework
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Confidential Computing for OpenPOWER. In Proceedings of the Sixteenth European Conference on Computer Systems . ACM, 294–310
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The speed of containers, the security of VMs
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PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments
Fan Mo, Hamed Haddadi, Kleomenis Katevas, Eduard Marin, Diego Perino, and Nicolas Kourtellis. 2021 · 2021
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Senate: A Maliciously-Secure { \{ MPC } \} Platform for Collaborative Analytics. In 30th USENIX Security Symposium . USENIX
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See through Gradients: Image Batch Recovery via GradInversion
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Cerebro: A Platform for Multi-Party Cryptographic Collaborative Learning. In 30th USENIX Security Symposium . USENIX
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