Ieee federated Machine Learning White Paper



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FederatedMachineLearning
e-Iraq estra.ar.en

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such as biometric data, photos, and other personal information will infringe on user privacy and could potentially violate laws and regulations. Therefore, FML is a technical solution for improving model performance without compromising user privacy. Taking personalized recommendation services as an examplerecommendation systems are often used to manage users flight, meeting, and hotel booking information and provide recommendations based on users personal information such as contacts, message, calendar, location, sports/sleep data, app usage etc. In these cases, horizontal FML techniques can provide a more secure and trustworthy solution to help prevent leakage of sensitive personal data. Authorized licensed use limited to University of Malta. Downloaded on December 24,2022 at 11:03:39 UTC from IEEE Xplore. Restrictions apply.

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REFERENCES
The following list of sources either has been referenced within this paper or maybe useful for additional reading IEEE Std 3652.1-2020, IEEE Guide for Architectural Framework and Application of Federated Machine Learning,
2020.
https://standards.ieee.org/standard/3652_1-2020.html
[2]
Jakub Konecný, H. Brendan McMahan, Daniel Ramage, and Peter Richtárik. a. Federated Optimization Distributed Machine Learning for On-Device Intelligence. CoRR abs (2016). Peter Kairouz, et al., Advances and Open Problems in Federated Learning, Foundations and Trends in Machine Learning Vol 4 Issue 1. http://dx.doi.org/10.1561/2200000083
. 2021.
[4]
Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, Han Yu. Federated Learning. ISBN
9781681736976, https://doi.org/10.2200/S00960ED2V01Y201910AIM043
. Morgan & Claypool Publishers, Dec 2019. H. Brendan McMahan, et al. Federated Learning of Deep Networks using Model Averaging. CoRR abs (2016). arXiv:1602.05629, 2016. Keith Bonawitz, et al. Practical Secure Aggregation for Privacy-Preserving Machine Learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (CCS ’17). ACM, New York, NY, USA, 1175–1191. 2017.
[7]
Qiang Yang, Yu Zhang, Wenyuan Dai and Sinno Jilin Pan, Transfer Learning. ISBN 9781139061773. DOI: https://doi.org/10.1017/9781139061773
. Cambridge University Press. Jan. 2020.

Authorized licensed use limited to University of Malta. Downloaded on December 24,2022 at 11:03:39 UTC from IEEE Xplore. Restrictions apply.



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