Flexible and lightweight toolbox for federated learning on edge devices
Biskup, Dean
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https://hdl.handle.net/2142/117764
Description
Title
Flexible and lightweight toolbox for federated learning on edge devices
Author(s)
Biskup, Dean
Issue Date
2022-11-17
Director of Research (if dissertation) or Advisor (if thesis)
Smaragdis, Paris
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Federated Learning
Machine Learning
Speech Enhancement
Language
eng
Abstract
As edge devices with data collection capabilities, such as cell phones, home assistants, or autonomous vehicles become more ubiquitous, there has been a rapid increase in the amount of data collected. While this data is valuable for machine learning applications, there is an increasing demand for data privacy and effective local data processing to lower network bandwidth requirements. Federated Learning has emerged as a central paradigm of machine learning to tackle these issues, allowing for collaborative learning between many edge devices without requiring the sharing of sensitive data. To facilitate research into federated learning on real-world devices, this thesis work introduces the Federated Learning on Edge Systems (FLoES) software library, a flexible and lightweight federated learning toolbox that is targeted to run on single-board computers. The source code is available at: https://www.github.com/dbisk/floes.
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