Challenges, Applications and Design Aspects of Federated Learning: A Survey

Rahman, K. M. Jawadur; Ahmed, Faisal; Akhter, Nazma; Hasan, Mohammad Al; Amin, Ruhul; Aziz, Kazi Ehsan; Islam, A. K. M. Muzahidul; Mukta, Md. Saddam Hossain; Islam, A. K. M. Najmul

doi:10.1109/access.2021.3111118

Cited by 88 publications

(67 citation statements)

References 90 publications

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“…Federated database systems (FDSs) [ 65 ] are systems that are able to combine multiple database entities and manage them as one overall system. This concept was proposed to achieve integration between multiple independent databases.…”

Section: Federated Learningmentioning

confidence: 99%

“…Moreover, FDS focuses on basic operations such as insert, delete, update, and other database operations. In this context, the two concepts are compared using [ 44 , 65 ]: Motivation: in FDBS, the main goal is to perform database operations over diverse and independent databases, while the main goal of FL is to process heterogeneous and independent databases to learn from data; Data identity: both can support non-IID databases; Centralization: both support the decentralization of database storage, but in FDBS, the processing is handled by a central server; Data access: in FDBS, unlike FL, the processing server has access to all data; Communication and data transfer: in FDBS all data are transferred in contrast to FL. …”

Section: Federated Learningmentioning

confidence: 99%

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Reviewing Federated Machine Learning and Its Use in Diseases Prediction

Moshawrab

Adda

Bouzouane

et al. 2023

Sensors

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Machine learning (ML) has succeeded in improving our daily routines by enabling automation and improved decision making in a variety of industries such as healthcare, finance, and transportation, resulting in increased efficiency and production. However, the development and widespread use of this technology has been significantly hampered by concerns about data privacy, confidentiality, and sensitivity, particularly in healthcare and finance. The “data hunger” of ML describes how additional data can increase performance and accuracy, which is why this question arises. Federated learning (FL) has emerged as a technology that helps solve the privacy problem by eliminating the need to send data to a primary server and collect it where it is processed and the model is trained. To maintain privacy and improve model performance, FL shares parameters rather than data during training, in contrast to the typical ML practice of sending user data during model development. Although FL is still in its infancy, there are already applications in various industries such as healthcare, finance, transportation, and others. In addition, 32% of companies have implemented or plan to implement federated learning in the next 12–24 months, according to the latest figures from KPMG, which forecasts an increase in investment in this area from USD 107 million in 2020 to USD 538 million in 2025. In this context, this article reviews federated learning, describes it technically, differentiates it from other technologies, and discusses current FL aggregation algorithms. It also discusses the use of FL in the diagnosis of cardiovascular disease, diabetes, and cancer. Finally, the problems hindering progress in this area and future strategies to overcome these limitations are discussed in detail.

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Section: Federated Learningmentioning

confidence: 99%

Section: Federated Learningmentioning

confidence: 99%

Reviewing Federated Machine Learning and Its Use in Diseases Prediction

Moshawrab

Adda

Bouzouane

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“… Li et al [ 27 ] 2021 A case study that can assist the design of a Federated Learning System, including aspects and research perspectives. Rahman et al [ 28 ] 2021 Issues related to the design of a Federated Learning based system. Zhang et al [ 29 ] 2021 Survey on FL based application areas.…”

Section: Introductionmentioning

confidence: 99%

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

et al. 2022

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Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare field. Traditionally, the healthcare system works based on centralized agents sharing their raw data. Therefore, huge vulnerabilities and challenges are still existing in this system. However, integrating with AI, the system would be multiple agent collaborators who are capable of communicating with their desired host efficiently. Again, FL is another interesting feature, which works decentralized manner; it maintains the communication based on a model in the preferred system without transferring the raw data. The combination of FL, AI, and XAI techniques can be capable of minimizing several limitations and challenges in the healthcare system. This paper presents a complete analysis of FL using AI for smart healthcare applications. Initially, we discuss contemporary concepts of emerging technologies such as FL, AI, XAI, and the healthcare system. We integrate and classify the FL-AI with healthcare technologies in different domains. Further, we address the existing problems, including security, privacy, stability, and reliability in the healthcare field. In addition, we guide the readers to solving strategies of healthcare using FL and AI. Finally, we address extensive research areas as well as future potential prospects regarding FL-based AI research in the healthcare management system.

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“…As can be observed, the above surveys have addressed various aspects of FL. To elaborate, the surveys in [1][2][3][4][5][6] were focused on the various challenges, opportunities, and future directions of Federated Learning. The surveys in [7][8][9][10][11] discussed edge computing, the Internet of Things (IoT), enabling technologies, solutions, and opportunities.…”

Section: Introductionmentioning

confidence: 99%

Federated Learning Design and FunctionalModels: Survey

John

Utomo

Rouniyar

et al. 2022

Preprint

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Federated learning is a multiple device collaboration setup designed tosolve machine learning problems under framework for aggregation andknowledge transfer in distributed local data. This distributed modelensures the privacy of data at each local node. Owing to its relevance,there has been extensive research activities and outcomes in federatedlearning with expanded applicability to different areas by the researchcommunity. As such, there is a vast research archive made available by thecommunity with research work and articles related to the various aspectsof federated learning such as applications, challenges, privacy, function-alities, and design. With respect to the function and design of federatedlearning, client selection, aggregation, knowledge transfer, managementof distributed data (Non-IID), Incentive of data and communication costare of paramount importance. Any effective design of federated learningrequires these aspects to be well considered.There are numerous surveyarticles found among the available literature that focus on its applica-tion and challenges, opportunities, data privacy and protection, as wellas on federated learning on internet of things, federated learning on edgecomputing, etc.1 In this paper, a review of the available literature on the various ele-ments of design and functionalities in federated learning has been carriedout with an aim to lay emphasis on the important challenges andresearch opportunities. More specifically, this work has endeavored tounderstand and summarize the various functional methods available,along with their techniques and goals. Additionally, it has strived toget a bird’s eye view of how various functions and designs of feder-ated learning have been used in applications, and how it has helpeduncover challenges and promising research directions for the future.

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Challenges, Applications and Design Aspects of Federated Learning: A Survey

Cited by 88 publications

References 90 publications

Reviewing Federated Machine Learning and Its Use in Diseases Prediction

Reviewing Federated Machine Learning and Its Use in Diseases Prediction

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

Federated Learning Design and FunctionalModels: Survey

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