World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
45
Citations
38208
World Ranking
6964
National Ranking
3044

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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