2026-09-20T20:47:27Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/117764 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Smaragdis, Paris 2022-12 application/pdf en text Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms The student, Dean Biskup, accepted the attached license on 2022-11-17 at 02:30. The student, Dean Biskup, submitted this Thesis for approval on 2022-11-17 at 02:40. This Thesis was approved for publication on 2022-11-17 at 16:57. DSpace SAF Submission Ingestion Package generated from Vireo submission #18593 on 2023-04-12 at 07:28:20 Flexible and lightweight toolbox for federated learning on edge devices Biskup, Dean 2022-11-17 Federated Learning Machine Learning Speech Enhancement 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. Thesis eng https://hdl.handle.net/2142/117764 Copyright 2022 Dean Biskup M.S. Thesis Electrical & Computer Engr University of Illinois at Urbana-Champaign Electrical & Computer Eng