University of Oulu

O. A. Wahab, A. Mourad, H. Otrok and T. Taleb, "Federated Machine Learning: Survey, Multi-Level Classification, Desirable Criteria and Future Directions in Communication and Networking Systems," in IEEE Communications Surveys & Tutorials, vol. 23, no. 2, pp. 1342-1397, Secondquarter 2021, doi: 10.1109/COMST.2021.3058573

Federated machine learning : survey, multi-level classification, desirable criteria and future directions in communication and networking systems

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Author: Wahab, Omar Abdel1; Mourad, Azzam2; Otrok, Hadi3;
Organizations: 1Department of Computer Science and Engineering, Universit´e du Qu´ebec en Outaouais, Gatineau, Canada
2Department of Mathematics and Computer Science, Lebanese American University, Beirut, Lebanon
3Department of EECS, Center on Cyber-Physical Systems, Khalifa University of Science and Technology, Abu Dhabi, UAE
4Department of Communications and Networking, Aalto University, Espoo 02150, Finland
5Centre for Wireless Communications (CWC), University of Oulu, Oulu 90570, Finland
6Department of Computer and Information Security, Sejong University, Seoul 05006, South Korea
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 8.3 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2021090144887
Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2021-09-01
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.

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Series: IEEE communications surveys and tutorials
ISSN: 1553-877X
ISSN-E: 2373-745X
ISSN-L: 1553-877X
Volume: 23
Issue: 2
Pages: 1342 - 1397
DOI: 10.1109/COMST.2021.3058573
OADOI: https://oadoi.org/10.1109/COMST.2021.3058573
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Funding: This work is partially funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) under grant number RGPIN- 2020-04707, by the Université du Québec en Outaouais (UQO), by the Lebanese American University, by Khalifa University of Science, Technology & Research (KUSTAR), by the European Union’s Horizon 2020 research and innovation program under grant agreement no101016509 (project CHARITY), by the Academy of Finland 6Genesis project under Grant No. 318927, and by the Academy of Finland CSN project under Grant No. 311654.
Academy of Finland Grant Number: 318927
311654
Detailed Information: 318927 (Academy of Finland Funding decision)
311654 (Academy of Finland Funding decision)
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