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Tayyebeh Jahani-Nezhad
Tayyebeh Jahani-Nezhad
Postdoctoral Researcher at Technische Universität Berlin (TUB)
Dirección de correo verificada de tu-berlin.de
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Citado por
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Año
CodedSketch: A coding scheme for distributed computation of approximated matrix multiplication
T Jahani-Nezhad, MA Maddah-Ali
IEEE Transactions on Information Theory, 2021
442021
Swiftagg+: Achieving asymptotically optimal communication loads in secure aggregation for federated learning
T Jahani-Nezhad, MA Maddah-Ali, S Li, G Caire
IEEE Journal on Selected Areas in Communications 41 (4), 977-989, 2023
292023
Berrut approximated coded computing: Straggler resistance beyond polynomial computing
T Jahani-Nezhad, MA Maddah-Ali
IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (1), 111-122, 2022
272022
SwiftAgg: Communication-Efficient and Dropout-Resistant Secure Aggregation for Federated Learning with Worst-Case Security Guarantees
T Jahani-Nezhad, MA Maddah-Ali, S Li, G Caire
2022 IEEE International Symposium on Information Theory (ISIT), 2022
222022
CodedSketch: Coded distributed computation of approximated matrix multiplication
T Jahani-Nezhad, MA Maddah-Ali
2019 IEEE International Symposium on Information Theory (ISIT), 2489-2493, 2019
162019
Optimal communication-computation trade-off in heterogeneous gradient coding
T Jahani-Nezhad, MA Maddah-Ali
IEEE Journal on Selected Areas in Information Theory 2 (3), 1002-1011, 2021
82021
CFO estimation in GFDM systems using extended Kalman filter
T Jahani-Nezhad, MR Taban, FS Tabataba
Electrical Engineering (ICEE), 2017 Iranian Conference on, 1815-1819, 2017
52017
Performance analysis of molecular spatial modulation (MSM) in diffusion based molecular MIMO communication systems
T Jahani-Nezhad, FS Tabataba
arXiv preprint arXiv:1809.05954, 2018
22018
Byzantine-Resistant Secure Aggregation for Federated Learning Based on Coded Computing and Vector Commitment
T Jahani-Nezhad, MA Maddah-Ali, G Caire
arXiv e-prints, arXiv: 2302.09913, 2023
12023
ByzSecAgg: A Byzantine-Resistant Secure Aggregation Scheme for Federated Learning Based on Coded Computing and Vector Commitment
T Jahani-Nezhad, MA Maddah-Ali, G Caire
arXiv preprint arXiv:2302.09913, 2023
2023
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