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Advances and Open Problems in Federated Learning

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Advances and Open Problems in Federated Learning

10 December 2019

Zachary B. Charles

Rachel Cummings

Rafael G. L. DÓliveira

Zachary Garrett

Phillip B. Gibbons

Zaïd Harchaoui

Lie He

Aleksandra Korolova

Oluwasanmi Koyejo

Tancrède Lepoint

Yang Liu

Mariana Raykova

Sebastian U. Stich

Florian Tramèr

Praneeth Vepakomma

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Papers citing "Advances and Open Problems in Federated Learning"

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Title
Boosting Privately: Privacy-Preserving Federated Extreme Boosting for Mobile Crowdsensing Yang Liu Zhuo Ma Ximeng Liu Siqi Ma Surya Nepal R. Deng FedML 144 64 0 24 Jul 2019
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and ProtectionIEEE Transactions on Knowledge and Data Engineering (TKDE), 2019 Yue Liu Zeyi Wen Zhaomin Wu Sixu Hu Naibo Wang Yuan N. Li Xu Liu Bingsheng He FedML 478 1,248 0 23 Jul 2019
Decentralized Deep Learning with Arbitrary Communication CompressionInternational Conference on Learning Representations (ICLR), 2019 Anastasia Koloskova Tao Lin Sebastian U. Stich Martin Jaggi FedML 303 251 0 22 Jul 2019
SoK: Differential PrivaciesProceedings on Privacy Enhancing Technologies (PoPETs), 2019 Damien Desfontaines Balázs Pejó 573 140 0 04 Jun 2019
SecureBoost: A Lossless Federated Learning Framework Kewei Cheng Tao Fan Yilun Jin Yang Liu Tianjian Chen Dimitrios Papadopoulos Qiang Yang FedML 244 653 0 25 Jan 2019
Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs Valentina Zantedeschi A. Bellet Marc Tommasi FedML 432 82 0 24 Jan 2019
Don't Use Large Mini-Batches, Use Local SGD Tao Lin Sebastian U. Stich Kumar Kshitij Patel Martin Jaggi 593 454 0 22 Aug 2018