Implementing and evaluating Google federated learning algorithms
Amid data privacy concerns, Federated Learning(FL) has emerged as a promising machine learning paradigm that enables privacy-preserving collaborative model training. However, there exists the need for a platform that matches data owners (supply) with model requesters (demand). This paper will dee...
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| Format: | Final Year Project |
| Language: | English |
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Nanyang Technological University
2021
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| Online Access: | https://hdl.handle.net/10356/148007 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Amid data privacy concerns, Federated Learning(FL) has emerged as a promising machine
learning paradigm that enables privacy-preserving collaborative model training. However, there
exists the need for a platform that matches data owners (supply) with model requesters
(demand). This paper will deep dive into some of the components of a working prototype of
CrowdFL, a platform for facilitating the crowdsourcing of FL models. It supports client
selection, model training, and reputation management, which are essential for the FL
crowdsourcing operations. By implementing model training on actual mobile devices, we
demonstrate that the platform improves model performance and training efficiency. |
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