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Proteus: A Self-Designing Range Filter


Jun 30, 2022
Eric R. Knorr, Baptiste Lemaire, Andrew Lim, Siqiang Luo, Huanchen Zhang, Stratos Idreos, Michael Mitzenmacher

* SIGMOD'22: Proceedings of the 2022 International Conference on Management of Data, June 2022, 1670-1684 
* 14 pages, 9 figures, originally published in the Proceedings of the 2022 International Conference on Management of Data (SIGMOD'22), ISBN: 9781450392495 

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QUIC-FL: Quick Unbiased Compression for Federated Learning


May 28, 2022
Ran Ben Basat, Shay Vargaftik, Amit Portnoy, Gil Einziger, Yaniv Ben-Itzhak, Michael Mitzenmacher


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Tabula: Efficiently Computing Nonlinear Activation Functions for Secure Neural Network Inference


Mar 05, 2022
Maximilian Lam, Michael Mitzenmacher, Vijay Janapa Reddi, Gu-Yeon Wei, David Brooks


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Communication-Efficient Federated Learning via Robust Distributed Mean Estimation


Aug 19, 2021
Shay Vargaftik, Ran Ben Basat, Amit Portnoy, Gal Mendelson, Yaniv Ben-Itzhak, Michael Mitzenmacher

* A technical report that extends arXiv:2105.08339 

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Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix


Jun 10, 2021
Maximilian Lam, Gu-Yeon Wei, David Brooks, Vijay Janapa Reddi, Michael Mitzenmacher

* ICML 2021 

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DRIVE: One-bit Distributed Mean Estimation


Jun 02, 2021
Shay Vargaftik, Ran Ben Basat, Amit Portnoy, Gal Mendelson, Yaniv Ben-Itzhak, Michael Mitzenmacher


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How to send a real number using a single bit (and some shared randomness)


Oct 08, 2020
Ran Ben-Basat, Michael Mitzenmacher, Shay Vargaftik


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Queues with Small Advice


Jun 27, 2020
Michael Mitzenmacher

* 16 pages, draft version, to be submitted, subject to cahnge 

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