World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
45
Citations
38208
World Ranking
6965
National Ranking
3045

H. Brendan McMahan publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where H. Brendan McMahan sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 70 publications — 2nd percentile

2% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

H. Brendan McMahan D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where H. Brendan McMahan sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 45 D-Index — 51st percentile

51% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

H. Brendan McMahan is affiliated with Google in the United States. Their research primarily lies within the field of Computer Science, with specific focus on several subfields including Artificial Intelligence, Computer Science Applications, Radiology, Nuclear Medicine and Imaging, Management Science and Operations Research, and Computational Theory and Mathematics.

The topics extensively covered in their work include Privacy-Preserving Technologies in Data, Mobile Crowdsensing and Crowdsourcing, Stochastic Gradient Optimization Techniques, Cryptography and Data Security, Machine Learning and ELM, MRI in cancer diagnosis, and Advanced Bandit Algorithms Research.

The scientist has contributed to multiple recent papers, several of which are published through prominent venues. These include:

  • Advances and Open Problems in Federated Learning, 2020, Foundations and Trends® in Machine Learning
  • A Field Guide to Federated Optimization, 2021, arXiv (Cornell University)
  • Adaptive Federated Optimization, 2020, arXiv (Cornell University)
  • Is Local SGD Better than Minibatch SGD?, 2020, arXiv (Cornell University)
  • Training Production Language Models without Memorizing User Data, 2020, arXiv (Cornell University)

Their frequent co-authors reflect collaborative work across several researchers in the field, including Peter Kairouz, Zachary Charles, Zachary Garrett, Jakub Konečný, and Sebastian U. Stich.

Regarding publication venues, most of the scientist's work appears in arXiv (Cornell University), with at least five papers, as well as in Foundations and Trends® in Machine Learning with one publication.

Best Publications

  • Communication-Efficient Learning of Deep Networks from Decentralized Data

    H. Brendan McMahan;Eider Moore;Daniel Ramage;Seth Hampson

  • Deep Learning with Differential Privacy

    Martin Abadi;Andy Chu;Ian Goodfellow;H. Brendan McMahan

  • Advances and Open Problems in Federated Learning

    Peter Kairouz;H. Brendan McMahan;Brendan Avent;Aurélien Bellet

  • Federated Learning: Strategies for Improving Communication Efficiency

    Jakub Konečný;H. Brendan McMahan;Felix X. Yu;Peter Richtarik

  • Practical Secure Aggregation for Privacy Preserving Machine Learning.

    Kallista A. Bonawitz;Vladimir Ivanov;Ben Kreuter;Antonio Marcedone

  • Towards Federated Learning at Scale: System Design

    Kallista A. Bonawitz;Hubert Eichner;Wolfgang Grieskamp;Dzmitry Huba

  • Federated Optimization: Distributed Machine Learning for On-Device Intelligence

    Jakub Konečný;H. Brendan McMahan;Daniel Ramage;Peter Richtarik

  • Federated Learning of Deep Networks using Model Averaging

    H. Brendan McMahan;Eider Moore;Daniel Ramage;Blaise Aguera y Arcas

  • Advances and open problems in federated learning

    Peter Kairouz;H. Brendan McMahan;Brendan Avent;Aurélien Bellet

  • Ad click prediction: a view from the trenches

    H. Brendan McMahan;Gary Holt;D. Sculley;Michael Young

  • Online convex optimization in the bandit setting: gradient descent without a gradient

    Abraham D. Flaxman;Adam Tauman Kalai;H. Brendan McMahan

  • Learning Differentially Private Recurrent Language Models

    H. Brendan McMahan;Daniel Ramage;Kunal Talwar;Li Zhang

  • Federated Optimization: Distributed Optimization Beyond the Datacenter

    Jakub Konečný;H. Brendan McMahan;Daniel Ramage

  • LEAF: A Benchmark for Federated Settings

    Sebastian Caldas;Peter Wu;Tian Li;Jakub Konecný

  • Robust Submodular Observation Selection

    Andreas Krause;H. Brendan McMahan;Carlos Guestrin;Anupam Gupta

  • cpSGD: communication-efficient and differentially-private distributed SGD

    Naman Agarwal;Ananda Theertha Suresh;Felix Yu;Sanjiv Kumar

  • Planning in the presence of cost functions controlled by an adversary

    H. Brendan McMahan;Geoffrey J. Gordon;Avrim Blum

  • Practical Secure Aggregation for Federated Learning on User-Held Data

    Kallista A. Bonawitz;Vladimir Ivanov;Ben Kreuter;Antonio Marcedone

  • Adaptive Bound Optimization for Online Convex Optimization

    H. Brendan McMahan;Matthew J. Streeter

  • Expanding the Reach of Federated Learning by Reducing Client Resource Requirements

    Sebastian Caldas;Jakub Konecný;H. Brendan McMahan;Ameet Talwalkar

  • Adaptive Federated Optimization

    Sashank Reddi;Zachary Charles;Manzil Zaheer;Zachary Garrett

Frequent Co-Authors

Daniel Ramage
Daniel Ramage Google (United States)
Felix X. Yu
Felix X. Yu Google (United States)
Kunal Talwar
Kunal Talwar Apple (United States)
Li Zhang
Li Zhang Google (United States)
Sanjiv Kumar
Sanjiv Kumar Google (United States)
Geoffrey J. Gordon
Geoffrey J. Gordon Carnegie Mellon University
Ameet Talwalkar
Ameet Talwalkar Carnegie Mellon University
Martin Jaggi
Martin Jaggi École Polytechnique Fédérale de Lausanne
Nathan Srebro
Nathan Srebro Toyota Technological Institute at Chicago
Dawn Song
Dawn Song University of California, Berkeley

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