Federated machine learning : survey, multi-level classification, desirable criteria and future directions in communication and networking systems
Wahab, Omar Abdel; Mourad, Azzam; Otrok, Hadi; Taleb, Tarik (2021-02-10)
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
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
https://urn.fi/URN:NBN:fi-fe2021090144887
Tiivistelmä
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.
Kokoelmat
- Avoin saatavuus [34573]
Samankaltainen aineisto
Näytetään aineisto, joilla on samankaltaisia nimekkeitä, tekijöitä tai asiasanoja.
-
Linking learning behavior analytics and learning science concepts : designing a learning analytics dashboard for feedback to support learning regulation
Sedrakyan, Gayane; Malmberg, Jonna; Verbert, Katrien; Järvelä, Sanna; Kirschner, Paul A.
Computers in human behavior (Elsevier, 06.05.2018) -
Learning enablers, learning outcomes, learning paths, and their relationships in organizational learning and change
Haho, Päivi
Acta Universitatis Ouluensis. C, Technica : 479 (University of Oulu, 31.01.2014) -
Bridging learning sciences, machine learning and affective computing for understanding cognition and affect in collaborative learning
Järvelä, Sanna; Gašević, Dragan; Seppänen, Tapio; Pechenizkiy, Mykola; Kirschner, Paul A.
British journal of educational technology : 6 (John Wiley & Sons, 06.03.2020)