Practice of FATE: Performance Improving Techniques
Pages 205 - 210
Abstract
Federated Learning is a type of privacy-preserving machine learning technology, which enables different parties to build a common, robust machine learning model together without sharing private data.
The concept of Federated Learning was introduced by Google in 2017. Since then, there has been rapid development in this field. Lots of promising technical frameworks, such as Paddle FL, PySyft, FedML, and Tensorflow Federated have emerged. Among them, FATE is one of the most popular frameworks.
In our practice of Federated Learning, we also choose FATE as the basic technical framework. However, FATE is inapplicable for some scenarios, especially when we try to train a model on a large scale of data.
In this paper, we will describe the performance bottlenecks we encountered during the practice of FATE and several performance improving techniques.
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Index Terms
- Practice of FATE: Performance Improving Techniques
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Published In
February 2023
619 pages
ISBN:9781450398411
DOI:10.1145/3587716
Copyright © 2023 ACM.
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Published: 07 September 2023
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ICMLC 2023
ICMLC 2023: 2023 15th International Conference on Machine Learning and Computing
February 17 - 20, 2023
Zhuhai, China
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