M
Martin Jaggi
Researcher at École Polytechnique Fédérale de Lausanne
Publications - 116
Citations - 7070
Martin Jaggi is an academic researcher from École Polytechnique Fédérale de Lausanne. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 22, co-authored 111 publications receiving 3360 citations. Previous affiliations of Martin Jaggi include University of California, Berkeley.
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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.
Proceedings Article
On the Relationship between Self-Attention and Convolutional Layers
TL;DR: This work proves that a multi-head self-attention layer with sufficient number of heads is at least as expressive as any convolutional layer, which provides evidence that attention layers can perform convolution and, indeed, they often learn to do so in practice.
Proceedings Article
Ensemble Distillation for Robust Model Fusion in Federated Learning
TL;DR: This work proposes ensemble distillation for model fusion, i.e. training the central classifier through unlabeled data on the outputs of the models from the clients, which allows flexible aggregation over heterogeneous client models that can differ e.g. in size, numerical precision or structure.
Proceedings Article
A Unified Theory of Decentralized SGD with Changing Topology and Local Updates
TL;DR: In this article, a unified convergence analysis of decentralized SGD methods is presented for smooth SGD problems and the convergence rates interpolate between heterogeneous (non-identically distributed data) and iid-data settings, recovering linear convergence rates in many special cases, for instance for over-parametrized models.