THESIS
2019
ix, 42 pages : illustrations ; 30 cm
Abstract
The scarcity of data and isolated data islands encourage different organizations to share
data with each other to train machine learning models. However, there are increasing
concerns on the problems of data privacy and security, which urges people to seek a solution
such as Federated Learning (FL) to share training data without violating data privacy.
Google first introduced their solution for mobile devices in which users can form a federation
and train a powerful model cooperatively, without leaking their own data. WeBank
developed their Federated Transfer Learning (FTL) which extends FL applicable to more
scenarios. However, the benefits come with a cost of extra computation and communication
consumption, resulting in efficiency problems. In order to efficiently deploy and s...[
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The scarcity of data and isolated data islands encourage different organizations to share
data with each other to train machine learning models. However, there are increasing
concerns on the problems of data privacy and security, which urges people to seek a solution
such as Federated Learning (FL) to share training data without violating data privacy.
Google first introduced their solution for mobile devices in which users can form a federation
and train a powerful model cooperatively, without leaking their own data. WeBank
developed their Federated Transfer Learning (FTL) which extends FL applicable to more
scenarios. However, the benefits come with a cost of extra computation and communication
consumption, resulting in efficiency problems. In order to efficiently deploy and scale
up Federated Learning solutions in production environment, we need a deep understanding
on how the infrastructure affects the efficiency. This thesis tries to answer this question
by quantitatively measuring real-word Federated Learning applications (TFF and FATE)
on Google Cloud. According to the results of carefully designed experiments, we present
the bottlenecks of each applications which can assist the future optimizations.
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