loading
Papers

Research.Publish.Connect.

Paper

Authors: Chamatidis Ilias 1 and Spathoulas Georgios 2

Affiliations: 1 Department of Computer Science and Biomedical Informatics, University of Thessaly, Greece ; 2 Department of Computer Science and Biomedical Informatics, University of Thessaly, Greece, Center for Cyber and Information Security, Norwegian University of Science and Technology, Gjovik and Norway

ISBN: 978-989-758-359-9

Keyword(s): Deep Learning, Federated Learning, Blockchain, Security, Privacy, Integrity, Incentives.

Abstract: Machine learning and especially deep learning are appropriate for solving multiple problems in various domains. Training such models though, demands significant processing power and requires large data-sets. Federated learning is an approach that merely solves these problems, as multiple users constitute a distributed network and each one of them trains a model locally with his data. This network can cumulatively sum up significant processing power to conduct training efficiently, while it is easier to preserve privacy, as data does not leave its owner. Nevertheless, it has been proven that federated learning also faces privacy and integrity issues. In this paper a general enhanced federated learning framework is presented. Users may provide data or the required processing power or participate just in order to train their models. Homomorphic encryption algorithms are employed to enable model training on encrypted data. Blockchain technology is used as smart contracts coordinate the wo rk-flow and the commitments made between all participating nodes, while at the same time, tokens exchanges between nodes provide the required incentives for users to participate in the scheme and to act legitimately. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.209.80.87

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Ilias, C. and Georgios, S. (2019). Machine Learning for All: A More Robust Federated Learning Framework.In Proceedings of the 5th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP, ISBN 978-989-758-359-9, pages 544-551. DOI: 10.5220/0007571705440551

@conference{icissp19,
author={Chamatidis Ilias. and Spathoulas Georgios.},
title={Machine Learning for All: A More Robust Federated Learning Framework},
booktitle={Proceedings of the 5th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,},
year={2019},
pages={544-551},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007571705440551},
isbn={978-989-758-359-9},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,
TI - Machine Learning for All: A More Robust Federated Learning Framework
SN - 978-989-758-359-9
AU - Ilias, C.
AU - Georgios, S.
PY - 2019
SP - 544
EP - 551
DO - 10.5220/0007571705440551

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.