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Federated Machine Learning

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Wahab, Omar Abdel
dc.contributor.author Otrok, Hadi
dc.contributor.author Mourad, Azzam
dc.contributor.author Taleb, Tarik
dc.date.accessioned 2021-05-26T07:05:20Z
dc.date.available 2021-05-26T07:05:20Z
dc.date.issued 2021-02
dc.identifier.citation Wahab , O A , Otrok , H , Mourad , A & Taleb , T 2021 , ' Federated Machine Learning : Survey, Multi-Level Classification, Desirable Criteria and Future Directions in Communication and Networking Systems ' , IEEE Communications Surveys and Tutorials , vol. 23 , no. 2 , 9352033 , pp. 1342-1397 . https://doi.org/10.1109/COMST.2021.3058573 en
dc.identifier.issn 1553-877X
dc.identifier.other PURE UUID: 6f46f228-7682-43ef-82ee-15faf11e2b04
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/6f46f228-7682-43ef-82ee-15faf11e2b04
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85100831147&partnerID=8YFLogxK
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/63040075/ELEC_Wahab_etal_Federated_Machine_Learning_IEEECOMST_2021_acceptedauthormanuscript.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/107753
dc.description.abstract The communication and networking field is hungry for machine learning decision-making solutions to replace the traditional model-driven approaches that proved to be not rich enough for seizing the ever-growing complexity and heterogeneity of the modern systems in the field. Traditional machine learning solutions assume the existence of (cloud-based) central entities that are in charge of processing the data. Nonetheless, the difficulty of accessing private data, together with the high cost of transmitting raw data to the central entity gave rise to a decentralized machine learning approach called Federated Learning. The main idea of federated learning is to perform an on-device collaborative training of a single machine learning model without having to share the raw training data with any third-party entity. Although few survey articles on federated learning already exist in the literature, the motivation of this survey stems from three essential observations. The first one is the lack of a fine-grained multi-level classification of the federated learning literature, where the existing surveys base their classification on only one criterion or aspect. The second observation is that the existing surveys focus only on some common challenges, but disregard other essential aspects such as reliable client selection, resource management and training service pricing. The third observation is the lack of explicit and straightforward directives for researchers to help them design future federated learning solutions that overcome the state-of-the-art research gaps. To address these points, we first provide a comprehensive tutorial on federated learning and its associated concepts, technologies and learning approaches. We then survey and highlight the applications and future directions of federated learning in the domain of communication and networking. Thereafter, we design a three-level classification scheme that first categorizes the federated learning literature based on the high-level challenge that they tackle. Then, we classify each high-level challenge into a set of specific low-level challenges to foster a better understanding of the topic. Finally, we provide, within each low-level challenge, a fine-grained classification based on the technique used to address this particular challenge. For each category of high-level challenges, we provide a set of desirable criteria and future research directions that are aimed to help the research community design innovative and efficient future solutions. To the best of our knowledge, our survey is the most comprehensive in terms of challenges and techniques it covers and the most fine-grained in terms of the multi-level classification scheme it presents. en
dc.format.extent 56
dc.format.extent 1342-1397
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher IEEE
dc.relation.ispartofseries IEEE Communications Surveys and Tutorials en
dc.relation.ispartofseries Volume 23, issue 2 en
dc.rights openAccess en
dc.title Federated Machine Learning en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Université du Québec en Outaouais
dc.contributor.department Khalifa University of Science and Technology
dc.contributor.department Lebanese American University
dc.contributor.department Mobile Network Softwarization and Service Customization
dc.contributor.department Department of Communications and Networking en
dc.subject.keyword Cloud computing
dc.subject.keyword Collaborative work
dc.subject.keyword Communication and Networking Systems.
dc.subject.keyword Data models
dc.subject.keyword Federated Learning
dc.subject.keyword Federated Learning Tutorial
dc.subject.keyword Machine learning
dc.subject.keyword Machine Learning
dc.subject.keyword Multi-Level Classification
dc.subject.keyword Security
dc.subject.keyword Servers
dc.subject.keyword Statistical Challenges
dc.subject.keyword Training
dc.subject.keyword Transfer Learning
dc.subject.keyword Tutorials
dc.identifier.urn URN:NBN:fi:aalto-202105267012
dc.identifier.doi 10.1109/COMST.2021.3058573
dc.type.version acceptedVersion


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