Towards Federated Learning at Scale: System Design
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-le...
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| creator | Bonawitz, Keith Eichner, Hubert Grieskamp, Wolfgang Huba, Dzmitry Ingerman, Alex Ivanov, Vladimir Kiddon, Chloe Konečný, Jakub Mazzocchi, Stefano McMahan, H. Brendan Van Overveldt, Timon Petrou, David Ramage, Daniel Roselander, Jason |
| description | 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. |
| doi_str_mv | 10.48550/arxiv.1902.01046 |
| format | Article |
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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
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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
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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.</abstract><doi>10.48550/arxiv.1902.01046</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Distributed, Parallel, and Cluster Computing Computer Science - Learning Statistics - Machine Learning |
| title | Towards Federated Learning at Scale: System Design |
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