H. Brendan McMahan
- Jakub Konečný
- Daniel Ramage
- Blaise Agüera y Arcas
- Peter Richtárik
- Eider Moore
- Seth Hampson
- Dave Bacon
- Martı́n Abadi
- Li Zhang
- Keith Bonawitz
- Karn Seth
- Peter Kairouz
- Sen Zhao
- Felix X. Yu
- Ananda Theertha Suresh
- Andy Chu
- Ian Goodfellow
- Ilya Mironov
- Kunal Talwar
- Vladimir Ivanov
- Ben Kreuter
- Antonio Marcedone
- Sarvar Patel
- Aaron Segal
H. Brendan McMahan
collaborator
line weight = collaboration weight
Works in this corpus
| Title | Position | Year | Cited |
|---|---|---|---|
| Deep Learning with Differential Privacy | middle | 2016 | 6,220 |
| Advances and Open Problems in Federated Learning | middle | 2020 | 5,383 |
| Communication-Efficient Learning of Deep Networks from Decentralized Data | first | 2016 | 5,190 |
| Practical Secure Aggregation for Privacy-Preserving Machine Learning | middle | 2017 | 3,744 |
| Federated Learning: Strategies for Improving Communication Efficiency | middle | 2016 | 3,050 |
| Federated Optimization: Distributed Machine Learning for On-Device Intelligence | middle | 2016 | 1,657 |
Is this one person?
This record is probably one person, but at least one signal is weak. Check the evidence below before relying on the totals.
- neutralNo ORCID on this record, so its identity rests on inference alone.
- supports2 distinct name form(s) across this row's works, counting a spelled-out given name and its initial as one form.
- supportsAt most 1 distinct institution(s) inside any five-year window, which is a normal career.
- supports94% of this row's works sit in its single largest field.
- neutralConfidence is judged on the 6 work(s) this corpus holds, not on the author's whole output. A single-work row carries little evidence either way.