Anit Kumar Sahu

1.5k total citations
28 papers, 309 citations indexed

About

Anit Kumar Sahu is a scholar working on Artificial Intelligence, Computer Networks and Communications and Computational Mechanics. According to data from OpenAlex, Anit Kumar Sahu has authored 28 papers receiving a total of 309 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Artificial Intelligence, 14 papers in Computer Networks and Communications and 8 papers in Computational Mechanics. Recurrent topics in Anit Kumar Sahu's work include Distributed Sensor Networks and Detection Algorithms (10 papers), Distributed Control Multi-Agent Systems (9 papers) and Stochastic Gradient Optimization Techniques (9 papers). Anit Kumar Sahu is often cited by papers focused on Distributed Sensor Networks and Detection Algorithms (10 papers), Distributed Control Multi-Agent Systems (9 papers) and Stochastic Gradient Optimization Techniques (9 papers). Anit Kumar Sahu collaborates with scholars based in United States, Serbia and India. Anit Kumar Sahu's co-authors include Soummya Kar, Manzil Zaheer, Ameet Talwalkar, Tian Li, Maziar Sanjabi, Virginia Smith, Salman Avestimehr, Dušan Jakovetić, J. Zico Kolter and Eric W. Tramel and has published in prestigious journals such as Proceedings of the IEEE, IEEE Transactions on Signal Processing and Neurocomputing.

In The Last Decade

Anit Kumar Sahu

27 papers receiving 301 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Anit Kumar Sahu United States 9 240 81 40 39 32 28 309
Sai Praneeth Karimireddy Switzerland 8 273 1.1× 66 0.8× 38 0.9× 49 1.3× 30 0.9× 19 301
Borja Balle United Kingdom 12 352 1.5× 38 0.5× 27 0.7× 35 0.9× 15 0.5× 37 407
Salman Salamatian United States 7 208 0.9× 55 0.7× 104 2.6× 23 0.6× 30 0.9× 15 276
Peva Blanchard Switzerland 3 547 2.3× 117 1.4× 48 1.2× 32 0.8× 27 0.8× 5 592
Julien Stainer Switzerland 4 552 2.3× 135 1.7× 49 1.2× 32 0.8× 27 0.8× 8 608
Manzil Zaheer United States 10 340 1.4× 30 0.4× 26 0.7× 82 2.1× 22 0.7× 23 397
Zhihua Zhang China 10 181 0.8× 156 1.9× 64 1.6× 22 0.6× 37 1.2× 19 327
Víctor Valls Ireland 8 237 1.0× 189 2.3× 157 3.9× 49 1.3× 33 1.0× 26 443
Amirhossein Reisizadeh United States 8 236 1.0× 144 1.8× 77 1.9× 15 0.4× 26 0.8× 15 302
Ilija Bogunovic Switzerland 8 100 0.4× 23 0.3× 20 0.5× 22 0.6× 20 0.6× 19 182

Countries citing papers authored by Anit Kumar Sahu

Since Specialization
Citations

This map shows the geographic impact of Anit Kumar Sahu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Anit Kumar Sahu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anit Kumar Sahu more than expected).

Fields of papers citing papers by Anit Kumar Sahu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Anit Kumar Sahu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Anit Kumar Sahu. The network helps show where Anit Kumar Sahu may publish in the future.

Co-authorship network of co-authors of Anit Kumar Sahu

This figure shows the co-authorship network connecting the top 25 collaborators of Anit Kumar Sahu. A scholar is included among the top collaborators of Anit Kumar Sahu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Anit Kumar Sahu. Anit Kumar Sahu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Bajović, Dragana, et al.. (2023). Large Deviations for Products of Non-Identically Distributed Network Matrices With Applications to Communication-Efficient Distributed Learning and Inference. IEEE Transactions on Signal Processing. 71. 1319–1333. 3 indexed citations
2.
Jakovetić, Dušan, et al.. (2023). Non-linear Gradient Mappings and Stochastic Optimization: a General Framework with Applications to Heavy-Tail Noise. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations
3.
Chennupati, Gopinath, et al.. (2023). Federated Self-Learning with Weak Supervision for Speech Recognition. 1–5. 1 indexed citations
4.
Lee, Sunwoo, Anit Kumar Sahu, Chaoyang He, & Salman Avestimehr. (2023). Partial model averaging in Federated Learning: Performance guarantees and benefits. Neurocomputing. 556. 126647–126647. 10 indexed citations
5.
Jakovetić, Dušan, Dragana Bajović, Anit Kumar Sahu, et al.. (2023). Nonlinear Gradient Mappings and Stochastic Optimization: A General Framework with Applications to Heavy-Tail Noise. SIAM Journal on Optimization. 33(2). 394–423. 4 indexed citations
6.
Ding, Jie, Eric W. Tramel, Anit Kumar Sahu, et al.. (2022). Federated Learning Challenges and Opportunities: An Outlook. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 8752–8756. 39 indexed citations
7.
Chen, Huili, Jie Ding, Eric W. Tramel, et al.. (2022). ActPerFL: Active Personalized Federated Learning. 1–5. 7 indexed citations
8.
Sahu, Anit Kumar, et al.. (2021). Simple and Efficient Hard Label Black-box Adversarial Attacks in Low Query Budget Regimes. 1461–1469. 17 indexed citations
9.
Sahu, Anit Kumar, et al.. (2021). Multiplicative Filter Networks. 19 indexed citations
10.
Sahu, Anit Kumar & Soummya Kar. (2020). Decentralized Zeroth-Order Constrained Stochastic Optimization Algorithms: Frank–Wolfe and Variants With Applications to Black-Box Adversarial Attacks. Proceedings of the IEEE. 108(11). 1890–1905. 13 indexed citations
11.
Xin, Ran, Anit Kumar Sahu, Soummya Kar, & Usman A. Khan. (2019). Distributed empirical risk minimization over directed graphs. 189–193. 1 indexed citations
12.
Sahu, Anit Kumar, Tian Li, Maziar Sanjabi, et al.. (2018). On the Convergence of Federated Optimization in Heterogeneous Networks.. arXiv (Cornell University). 135 indexed citations
13.
Sahu, Anit Kumar, Manzil Zaheer, & Soummya Kar. (2018). Towards Gradient Free and Projection Free Stochastic Optimization. arXiv (Cornell University). 3468–3477. 8 indexed citations
14.
Sahu, Anit Kumar, Dušan Jakovetić, & Soummya Kar. (2018). $\mathcal {CIRFE}$: A Distributed Random Fields Estimator. IEEE Transactions on Signal Processing. 66(18). 4980–4995. 11 indexed citations
15.
Bajović, Dragana, Dušan Jakovetić, Anit Kumar Sahu, & Soummya Kar. (2018). Large Deviations for Products of Non-I.i.d. Stochastic Matrices with Application to Distributed Detection. 1061–1065. 1 indexed citations
16.
Sahu, Anit Kumar, Dušan Jakovetić, Dragana Bajović, & Soummya Kar. (2018). Communication efficient distributed weighted non-linear least squares estimation. EURASIP Journal on Advances in Signal Processing. 2018(1). 4 indexed citations
17.
Sahu, Anit Kumar, Dušan Jakovetić, & Soummya Kar. (2018). CREDO: A Communication-Efficient Distributed Estimation Algorithm. 147. 516–520. 3 indexed citations
18.
Sahu, Anit Kumar & Soummya Kar. (2016). Distributed sequence prediction: A consensus+innovations approach. 92. 312–316. 1 indexed citations
19.
Sahu, Anit Kumar & Soummya Kar. (2016). Distributed generalized likelihood ratio tests: Fundamental limits and tradeoffs. 4573–4577. 1 indexed citations
20.
Sahu, Anit Kumar & Soummya Kar. (2014). Distributed sequential detection for Gaussian binary hypothesis testing: Heterogeneous networks. 2014 48th Asilomar Conference on Signals, Systems and Computers. 92. 723–727. 4 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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