Anit Kumar Sahu

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Soummya KarElectrical and Computer Engineering, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
Tian LiCarnegie Mellon UniversityVerified email at cmu.edu
Virginia SmithCarnegie Mellon UniversityVerified email at cmu.edu
Ameet TalwalkarCMUVerified email at cmu.edu
Manzil ZaheerGoogle ResearchVerified email at cmu.edu
Dusan JakoveticDept. of Math. and Informatics, Faculty of Sciences, Univ. of Novi Sad; COSTNET CA15109Verified email at uns.ac.rs
Maziar SanjabiMeta AIVerified email at fb.com
Dragana BajovicICONIC center, Faculty of Technical Sciences, University of Novi SadVerified email at uns.ac.rs
Zico KolterCarnegie Mellon UniversityVerified email at cs.cmu.edu
Devin WillmottBosch Center for Artificial IntelligenceVerified email at us.bosch.com
Gauri JoshiCarnegie Mellon UniversityVerified email at andrew.cmu.edu
Satya Narayan ShuklaMeta AIVerified email at cs.umass.edu
Usman A. KhanProfessor of Electrical Engineering at Tufts University, Amazon Scholar at Amazon RoboticsVerified email at ece.tufts.edu
Jianyu WangResearch Scientist at AppleVerified email at apple.com
Jose M. F. MouraPhilip L. and Marsha Dowd University Professor, Carnegie Mellon UniversityVerified email at ece.cmu.edu
Ran XinResearch Scientist, ByteDanceVerified email at bytedance.com
Salman AvestimehrDean's Professor and Director of USC-Amazon Center on Trustworthy AI, USCVerified email at usc.edu
Eric W. TramelDirector, ML Research @ Unlearn.aiVerified email at unlearn.ai
Jie DingAssistant Professor, University of Minnesota Twin CitiesVerified email at umn.edu
Rizal FathonyCarnegie Mellon UniversityVerified email at cs.cmu.edu

Anit Kumar Sahu

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Federated Learning Stochastic Optimization


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Federated learning: Challenges, methods, and future directions T Li, AK Sahu, A Talwalkar, V Smith IEEE signal processing magazine 37 (3), 50-60, 2020	2543	2020
Federated optimization in heterogeneous networks T Li, AK Sahu, M Zaheer, M Sanjabi, A Talwalkar, V Smith Proceedings of Machine learning and systems 2, 429-450, 2020	2146	2020
Matcha: Speeding up decentralized sgd via matching decomposition sampling J Wang, AK Sahu, Z Yang, G Joshi, S Kar 2019 Sixth Indian Control Conference (ICC), 299-300, 2019	128	2019
Feddane: A federated newton-type method T Li, AK Sahu, M Zaheer, M Sanjabi, A Talwalkar, V Smithy 2019 53rd Asilomar Conference on Signals, Systems, and Computers, 1227-1231, 2019	88	2019
Distributed stochastic optimization with gradient tracking over strongly-connected networks R Xin, AK Sahu, UA Khan, S Kar 2019 IEEE 58th Conference on Decision and Control (CDC), 8353-8358, 2019	84	2019
Convergence rates for distributed stochastic optimization over random networks D Jakovetic, D Bajovic, AK Sahu, S Kar 2018 IEEE Conference on Decision and Control (CDC), 4238-4245, 2018	57	2018
Distributed sequential detection for Gaussian shift-in-mean hypothesis testing AK Sahu, S Kar IEEE Transactions on Signal Processing 64 (1), 89-103, 2015	46*	2015
Multiplicative filter networks R Fathony, AK Sahu, D Willmott, JZ Kolter International Conference on Learning Representations, 2021	45	2021
Distributed zeroth order optimization over random networks: A Kiefer-Wolfowitz stochastic approximation approach AK Sahu, D Jakovetic, D Bajovic, S Kar 2018 IEEE Conference on Decision and Control (CDC), 4951-4958, 2018	45	2018
Distributed constrained recursive nonlinear least-squares estimation: Algorithms and asymptotics AK Sahu, S Kar, JMF Moura, HV Poor IEEE Transactions on Signal and Information Processing over Networks 2 (4 …, 2016	37	2016
Black-box adversarial attacks with bayesian optimization SN Shukla, AK Sahu, D Willmott, JZ Kolter arXiv preprint arXiv:1909.13857, 2019	31	2019
Federated learning: challenges, methods, and future directions. CoRR T Li, AK Sahu, A Talwalkar, V Smith arXiv preprint arXiv:1908.07873 3, 2019	31*	2019
Towards gradient free and projection free stochastic optimization AK Sahu, M Zaheer, S Kar The 22nd International Conference on Artificial Intelligence and Statistics …, 2019	30	2019
Recursive distributed detection for composite hypothesis testing: Nonlinear observation models in additive gaussian noise AK Sahu, S Kar IEEE Transactions on Information Theory 63 (8), 4797-4828, 2017	23*	2017
Federated learning challenges and opportunities: An outlook J Ding, E Tramel, AK Sahu, S Wu, S Avestimehr, T Zhang ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022	17	2022
Simple and efficient hard label black-box adversarial attacks in low query budget regimes SN Shukla, AK Sahu, D Willmott, Z Kolter Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data …, 2021	16	2021
Communication-efficient distributed strongly convex stochastic optimization: Non-asymptotic rates AK Sahu, D Jakovetic, D Bajovic, S Kar arXiv preprint arXiv:1809.02920, 2018	16	2018
: A Distributed Random Fields Estimator AK Sahu, D Jakovetić, S Kar IEEE Transactions on Signal Processing 66 (18), 4980-4995, 2018	16	2018
Communication optimality trade-offs for distributed estimation AK Sahu, D Jakovetic, S Kar arXiv preprint arXiv:1801.04050, 2018	11	2018
Federated optimization in heterogeneous networks. arXiv AK Sahu, T Li, M Sanjabi, M Zaheer, AS Talwalkar, V Smith Learning 2 (4), 2020	10*	2020

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