| Federated learning: Challenges, methods, and future directions T Li, AK Sahu, A Talwalkar, V Smith IEEE signal processing magazine 37 (3), 50-60, 2020 | 3689 | 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 | 3341 | 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 | 148 | 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 | 125 | 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 | 100 | 2019 |
| Multiplicative filter networks R Fathony, AK Sahu, D Willmott, JZ Kolter International Conference on Learning Representations, 2020 | 84 | 2020 |
| 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 | 62 | 2018 |
| 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 | 51 | 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 |
| 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 | 42 | 2016 |
| Towards gradient free and projection free stochastic optimization AK Sahu, M Zaheer, S Kar The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 37 | 2019 |
| Black-box adversarial attacks with bayesian optimization SN Shukla, AK Sahu, D Willmott, JZ Kolter arXiv preprint arXiv:1909.13857, 2019 | 35 | 2019 |
| 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 | 33 | 2022 |
| Federated learning: challenges, methods, and future directions. CoRR T Li, AK Sahu, A Talwalkar, V Smith arXiv preprint arXiv:1908.07873, 2019 | 29* | 2019 |
| Federated optimization in heterogeneous networks. arXiv 2018 T Li, AK Sahu, M Zaheer, M Sanjabi, A Talwalkar, V Smith arXiv preprint arXiv:1812.06127, 2020 | 26 | 2020 |
| 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 | 24* | 2017 |
| 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 | 23 | 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 |
| On the convergence of federated optimization in heterogeneous networks AK Sahu, T Li, M Sanjabi, M Zaheer, A Talwalkar, V Smith https://arxiv.org/abs/1812.06127v1 3, 3, 2018 | 15* | 2018 |
Soummya KarElectrical and Computer Engineering, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
