%0 Generic %1 bonawitz2019towards %A Bonawitz, Keith %A Eichner, Hubert %A Grieskamp, Wolfgang %A Huba, Dzmitry %A Ingerman, Alex %A Ivanov, Vladimir %A Kiddon, Chloe %A Konečný, Jakub %A Mazzocchi, Stefano %A McMahan, H. Brendan %A Van Overveldt, Timon %A Petrou, David %A Ramage, Daniel %A Roselander, Jason %D 2019 %K Federated_Learning %T Towards Federated Learning at Scale: System Design %U http://arxiv.org/abs/1902.01046 %X Federated Learning is a distributed machine learning approach which enables model training on a large corpus of decentralized data. We have built a scalable production system for Federated Learning in the domain of mobile devices, based on TensorFlow. In this paper, we describe the resulting high-level design, sketch some of the challenges and their solutions, and touch upon the open problems and future directions.

@misc{bonawitz2019towards, abstract = {Federated Learning is a distributed machine learning approach which enables model training on a large corpus of decentralized data. We have built a scalable production system for Federated Learning in the domain of mobile devices, based on TensorFlow. In this paper, we describe the resulting high-level design, sketch some of the challenges and their solutions, and touch upon the open problems and future directions.}, added-at = {2019-05-10T13:55:02.000+0200}, author = {Bonawitz, Keith and Eichner, Hubert and Grieskamp, Wolfgang and Huba, Dzmitry and Ingerman, Alex and Ivanov, Vladimir and Kiddon, Chloe and Konečný, Jakub and Mazzocchi, Stefano and McMahan, H. Brendan and Van Overveldt, Timon and Petrou, David and Ramage, Daniel and Roselander, Jason}, biburl = {https://www.bibsonomy.org/bibtex/287834b32446bd322516a0eebc779d2ba/straybird321}, description = {Towards Federated Learning at Scale: System Design - 1902.01046.pdf}, interhash = {dc08ac7ba01b39446356860421edede9}, intrahash = {87834b32446bd322516a0eebc779d2ba}, keywords = {Federated_Learning}, note = {cite arxiv:1902.01046}, timestamp = {2019-05-10T13:55:02.000+0200}, title = {Towards Federated Learning at Scale: System Design}, url = {http://arxiv.org/abs/1902.01046}, year = 2019 }