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Loki: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulation

https://doi.org/10.1109/SP54263.2024.00030

Zhao, Joshua C; Sharma, Atul; Elkordy, Ahmed Roushdy; Ezzeldin, Yahya H; Avestimehr, Salman; Bagchi, Saurabh (May 2024, IEEE Security and Privacy)

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Gomory-Hu Trees Over Wireless

https://doi.org/10.1109/LCOMM.2022.3155176

Dogan, Mine Gokce; Ezzeldin, Yahya H.; Fragouli, Christina (May 2022, IEEE Communications Letters)

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Quantization of Distributed Data for Learning

https://doi.org/10.1109/JSAIT.2021.3105359

Hanna, Osama A.; Ezzeldin, Yahya H.; Fragouli, Christina; Diggavi, Suhas (September 2021, IEEE Journal on Selected Areas in Information Theory)

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A coding approach to localization using landmarks

https://doi.org/10.1109/GLOBECOM42002.2020.9347982

Rebanal, Juan Carlo; Ezzeldin, Yahya H.; Fragouli, Christina; Tabuada, Paulo (December 2020, GLOBECOM)

Fully autonomous vehicles need the ability to localize without external help, for instance by using visual sensors together with a pre-loaded map of landmarks. In this paper we connect self-localization using landmarks with coding theory. This connection enables to translate Hamming distance properties to probabilistic localization guarantees given a certain number of errors in landmark identification; it also enables to leverage existing polynomial time decoding algorithms for localization. We present promising numerical evaluation results by simulating vehicle traveling paths along a road network generated from real data of a region in Washington D.C.
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The Approximate Capacity of Half-Duplex Line Networks

https://doi.org/10.1109/TIT.2020.2998184

Ezzeldin, Yahya H.; Cardone, Martina; Fragouli, Christina; Tuninetti, Daniela (July 2020, IEEE Transactions on Information Theory)

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Secure Communication over 1-2-1 Networks

https://doi.org/10.1109/ISIT.2018.8437607

Agarwal, Gaurav Kumar; Ezzeldin, Yahya H.; Cardone, Martina; Fragouli, Christina (June 2018, IEEE International Symposium on Information Theory (ISIT))

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