B
Badih Ghazi
Researcher at Google
Publications - 92
Citations - 4040
Badih Ghazi is an academic researcher from Google. The author has contributed to research in topics: Computer science & Communication complexity. The author has an hindex of 13, co-authored 71 publications receiving 1754 citations. Previous affiliations of Badih Ghazi include American University of Beirut & Massachusetts Institute of Technology.
Papers
More filters
Journal ArticleDOI
Advances and open problems in federated learning
Peter Kairouz,H. Brendan McMahan,Brendan Avent,Aurélien Bellet,Mehdi Bennis,Arjun Nitin Bhagoji,Kallista Bonawitz,Zachary Charles,Graham Cormode,Rachel Cummings,Rafael G. L. D'Oliveira,Hubert Eichner,Salim El Rouayheb,David Evans,Josh Gardner,Zachary Garrett,Adrià Gascón,Badih Ghazi,Phillip B. Gibbons,Marco Gruteser,Zaid Harchaoui,Chaoyang He,Lie He,Zhouyuan Huo,Ben Hutchinson,Justin Hsu,Martin Jaggi,Tara Javidi,Gauri Joshi,Mikhail Khodak,Jakub Konecní,Aleksandra Korolova,Farinaz Koushanfar,Sanmi Koyejo,Tancrède Lepoint,Yang Liu,Prateek Mittal,Mehryar Mohri,Richard Nock,Ayfer Ozgur,Rasmus Pagh,Hang Qi,Daniel Ramage,Ramesh Raskar,Mariana Raykova,Dawn Song,Weikang Song,Sebastian U. Stich,Ziteng Sun,Ananda Theertha Suresh,Florian Tramèr,Praneeth Vepakomma,Jianyu Wang,Li Xiong,Zheng Xu,Qiang Yang,Felix X. Yu,Han Yu,Sen Zhao +58 more
TL;DR: In this article, the authors describe the state-of-the-art in the field of federated learning from the perspective of distributed optimization, cryptography, security, differential privacy, fairness, compressed sensing, systems, information theory, and statistics.
Posted Content
Advances and Open Problems in Federated Learning
Peter Kairouz,H. Brendan McMahan,Brendan Avent,Aurélien Bellet,Mehdi Bennis,Arjun Nitin Bhagoji,Kallista Bonawitz,Zachary Charles,Graham Cormode,Rachel Cummings,Rafael G. L. D'Oliveira,Hubert Eichner,Salim El Rouayheb,David Evans,Josh Gardner,Zachary Garrett,Adrià Gascón,Badih Ghazi,Phillip B. Gibbons,Marco Gruteser,Zaid Harchaoui,Chaoyang He,Lie He,Zhouyuan Huo,Ben Hutchinson,Justin Hsu,Martin Jaggi,Tara Javidi,Gauri Joshi,Mikhail Khodak,Jakub Konečný,Aleksandra Korolova,Farinaz Koushanfar,Sanmi Koyejo,Tancrède Lepoint,Yang Liu,Prateek Mittal,Mehryar Mohri,Richard Nock,Ayfer Ozgur,Rasmus Pagh,Mariana Raykova,Hang Qi,Daniel Ramage,Ramesh Raskar,Dawn Song,Weikang Song,Sebastian U. Stich,Ziteng Sun,Ananda Theertha Suresh,Florian Tramèr,Praneeth Vepakomma,Jianyu Wang,Li Xiong,Zheng Xu,Qiang Yang,Felix X. Yu,Han Yu,Sen Zhao +58 more
TL;DR: Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges.
Posted Content
Scalable and Differentially Private Distributed Aggregation in the Shuffled Model.
TL;DR: A simple and more efficient protocol for aggregation in the shuffled model, where communication as well as error increases only polylogarithmically in the number of users, is proposed.
Posted Content
Sample-Optimal Average-Case Sparse Fourier Transform in Two Dimensions
TL;DR: The first sample-optimal sublinear time algorithms for the sparse Discrete Fourier Transform over a two-dimensional√n × √n grid are presented and match the lower bounds on sample complexity for their respective signal models.
Proceedings ArticleDOI
Sample-optimal average-case sparse Fourier Transform in two dimensions
TL;DR: In this paper, the authors presented the first sample-optimal sublinear time algorithms for the sparse Discrete Fourier Transform over a two-dimensional √ n × √n grid.