Modelling Audiological Preferences using Federated Learning

Szatmari, Tiberiu-Ioan; Petersen, Michael Kai; Korzepa, Maciej Jan; Giannetsos, Thanassis

doi:10.1145/3386392.3399560

Cited by 11 publications

(7 citation statements)

References 5 publications

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“…Based on a modified DL4J library integrated on smartphones, two CNNs are performed by employing multi-channel sensing data, indicating a high performance in terms of high training accuracy and communication delays reduced by 53% with a Ring-scheduler approach. Smartphones have been utilized in [123] to implement an FL algorithm for federated mobile healthcare, with a focus on solving the cold start issue caused by slow data generation and computation of certain mobile devices in the cooperative FL process. For large-scale healthcare cooperation, blockchain [124] has been emerged as a viable solution that can be integrated with FL for building decentralized healthcare systems involving a large number of medical entities acted as data workers [125].…”

Section: Smart Industrymentioning

confidence: 99%

Federated Learning for Internet of Things: A Comprehensive Survey

Nguyen

Ding

Pathirana

et al. 2021

IEEE Commun. Surv. Tutorials

574

182

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Section: Smart Industrymentioning

confidence: 99%

Federated Learning for Internet of Things: A Comprehensive Survey

Nguyen

Ding

Pathirana

et al. 2021

IEEE Commun. Surv. Tutorials

574

182

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“…The case study explores an architecture deployed at ive institutions that hold a total of 20,000 patient records. Szatmari et al [32] propose a standard FL architecture to train learning models for the personalization of hearing aids. Sheller et al [33] apply FL to train a model to identify cancer-afected brain tissue using as a case study the BraTS 2017 dataset.…”

Section: Casementioning

confidence: 99%

Federated Learning for Healthcare: Systematic Review and Architecture Proposal

Antunes

Costa

Küderle

et al. 2022

ACM Trans. Intell. Syst. Technol.

196

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The use of machine learning (ML) with electronic health records (EHR) is growing in popularity as a means to extract knowledge that can improve the decision-making process in healthcare. Such methods require training of high-quality learning models based on diverse and comprehensive datasets, which are hard to obtain due to the sensitive nature of medical data from patients. In this context, federated learning (FL) is a methodology that enables the distributed training of machine learning models with remotely hosted datasets without the need to accumulate data and, therefore, compromise it. FL is a promising solution to improve ML-based systems, better aligning them to regulatory requirements, improving trustworthiness and data sovereignty. However, many open questions must be addressed before the use of FL becomes widespread. This article aims at presenting a systematic literature review on current research about FL in the context of EHR data for healthcare applications. Our analysis highlights the main research topics, proposed solutions, case studies, and respective ML methods. Furthermore, the article discusses a general architecture for FL applied to healthcare data based on the main insights obtained from the literature review. The collected literature corpus indicates that there is extensive research on the privacy and confidentiality aspects of training data and model sharing, which is expected given the sensitive nature of medical data. Studies also explore improvements to the aggregation mechanisms required to generate the learning model from distributed contributions and case studies with different types of medical data.

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“…Secret sharing [156] is a cryptographic technique guaranteeing that a secret consisting of n shares can be reconstructed only when a sufficient number of shares are combined. Secret sharing has been used in many FL frameworks to achieve privacy preservation [38,59,113,126,157,169,198,224]. For example, Bonawitz et al [13] proposed a practical and secure framework for the FL based on the secret sharing.…”

Section: Encryption-based Ppflmentioning

confidence: 99%

A Comprehensive Survey of Privacy-preserving Federated Learning

2021

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The past four years have witnessed the rapid development of federated learning (FL). However, new privacy concerns have also emerged during the aggregation of the distributed intermediate results. The emerging privacy-preserving FL (PPFL) has been heralded as a solution to generic privacy-preserving machine learning. However, the challenge of protecting data privacy while maintaining the data utility through machine learning still remains. In this article, we present a comprehensive and systematic survey on the PPFL based on our proposed 5W-scenario-based taxonomy. We analyze the privacy leakage risks in the FL from five aspects, summarize existing methods, and identify future research directions.

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Modelling Audiological Preferences using Federated Learning

Cited by 11 publications

References 5 publications

Federated Learning for Internet of Things: A Comprehensive Survey

Federated Learning for Internet of Things: A Comprehensive Survey

Federated Learning for Healthcare: Systematic Review and Architecture Proposal

A Comprehensive Survey of Privacy-preserving Federated Learning

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