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Modelling Audiological Preferences using Federated Learning

Published: 13 July 2020 Publication History

Abstract

Patient-centric adaptation of audiological preferences across different contexts is a challenging task, as traditional clinical measurements of audibility do not reflect the cognitive perception of speech nor binaural loudness of sounds in different contexts. Smartphone-based machine learning personalization systems have the potential to address this issue in real-world listening scenarios, however, the necessary training datasets are not currently available. As hearing healthcare medical data is of a highly private nature, a framework is proposed, combining federated learning (FL) and secret sharing in the context of hearing aids with the goal of training models locally while preserving the individual user's privacy. We demonstrate an application of such a system with a simplified domain defined by the MNIST digit classification task.

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Cited By

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  • (2024)ESB-FL: Efficient and Secure Blockchain-Based Federated Learning With Fair PaymentIEEE Transactions on Big Data10.1109/TBDATA.2022.317717010:6(761-774)Online publication date: Dec-2024
  • (2024)Federated Learning Via Nonorthogonal Multiple Access for UAV-Assisted Internet of ThingsIEEE Internet of Things Journal10.1109/JIOT.2024.341378011:17(27994-28006)Online publication date: 1-Sep-2024
  • (2024)Enhancing the Long-Term Sustainability of Healthcare Data Optimization Through Deep Federated Learning2024 Third International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS)10.1109/INCOS59338.2024.10527505(1-6)Online publication date: 14-Mar-2024
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cover image ACM Conferences
UMAP '20 Adjunct: Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization
July 2020
395 pages
ISBN:9781450379502
DOI:10.1145/3386392
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 13 July 2020

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Author Tags

  1. federated learning
  2. hearing healthcare
  3. personalization
  4. privacy
  5. recommender systems
  6. secretsharing

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UMAP '20
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Overall Acceptance Rate 162 of 633 submissions, 26%

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Cited By

View all
  • (2024)ESB-FL: Efficient and Secure Blockchain-Based Federated Learning With Fair PaymentIEEE Transactions on Big Data10.1109/TBDATA.2022.317717010:6(761-774)Online publication date: Dec-2024
  • (2024)Federated Learning Via Nonorthogonal Multiple Access for UAV-Assisted Internet of ThingsIEEE Internet of Things Journal10.1109/JIOT.2024.341378011:17(27994-28006)Online publication date: 1-Sep-2024
  • (2024)Enhancing the Long-Term Sustainability of Healthcare Data Optimization Through Deep Federated Learning2024 Third International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS)10.1109/INCOS59338.2024.10527505(1-6)Online publication date: 14-Mar-2024
  • (2024)A multifaceted survey on privacy preservation of federated learning: progress, challenges, and opportunitiesArtificial Intelligence Review10.1007/s10462-024-10766-757:7Online publication date: 21-Jun-2024
  • (2024)Federated learning for digital healthcare: concepts, applications, frameworks, and challengesComputing10.1007/s00607-024-01317-7106:9(3113-3150)Online publication date: 10-Jul-2024
  • (2022)Applications and Challenges of Federated Learning Paradigm in the Big Data Era with Special Emphasis on COVID-19Big Data and Cognitive Computing10.3390/bdcc60401276:4(127)Online publication date: 26-Oct-2022
  • (2022)Sustainability of Healthcare Data Analysis IoT-Based Systems Using Deep Federated LearningIEEE Internet of Things Journal10.1109/JIOT.2021.31036359:10(7338-7346)Online publication date: 15-May-2022
  • (2021)Federated Learning for Internet of Things: A Comprehensive SurveyIEEE Communications Surveys & Tutorials10.1109/COMST.2021.307543923:3(1622-1658)Online publication date: Nov-2022

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