Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/148007
Title: Implementing and evaluating Google federated learning algorithms
Authors: Cicilia Helena
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Cicilia Helena (2021). Implementing and evaluating Google federated learning algorithms. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148007
Project: SCSE20 - 0077
Abstract: 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.
URI: https://hdl.handle.net/10356/148007
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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