Federated Learning for Smart Healthcare: A Survey

Nguyen, Dinh C.; Pham, Quoc-Viet; Pathirana, Pubudu N.; Ding, Ming; Seneviratne, Aruna; Lin, Zihuai; Dobre, Octavia A.; Hwang, Won-Joo

doi:10.1145/3501296

Cited by 337 publications

(124 citation statements)

References 87 publications

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“…When these UEs and instruments use radio, microwaves, etc. to transmit signals, they form a wireless body area network (WBAN) [141] that has the capability to monitor human/animal vital statistics and deliver health data in real time. Furthermore, the data produced by these IoT UEs and devices may be observed to extract patients' symptoms.…”

Section: B Iot Applicationsmentioning

confidence: 99%

Federated Reinforcement Learning for 6G Wireless Networks: Fundamentals, Challenges and Future Research Trends

Das¹,

Mudi²,

Rahman³

et al. 2023

Preprint

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<p>Smart services based on the Internet of Things (IoT) are likely to grow in popularity in the forthcoming years, necessitating the improvement of fifth-generation (5G) cellular networks upgrade of future networks from their present state. Despite the fact that the 5G cellular networks may manage a diversity of IoT services, they may not be able to fully meet the requirements of emerging smart applications due to their limitations that, in many cases, could be overcome by applying artificial intelligence (AI). Therefore, sixth–generation (6G) wireless technologies are being developed to address the limitations of 5G networks. Traditional machine learning (ML) techniques are driven in a centralized way. However, the huge volume of produced wireless data, the confidentiality concerns, and the growing computing competencies of wireless edge devices have led to the exposure of a promising solution in a decentralized way which is called distributed learning. This paper provides a comprehensive analysis of distributed learning (e.g., federated learning (FL), multi–agent reinforcement learning (MARL)–based FL framework) and how to deploy in an effective and efficient way for wireless networks. Moreover, we describe a timely comprehensive review of the role of FL in facilitating 6G enabling technologies, such as mobile edge computing, network slicing, satellite communications, terahertz links, blockchain, and semantic communications. Also, we identify and discuss several open research issues related to FL–empowered 6G wireless networks. In particular, we focus on FL for enabling an extensive range of smart services and applications. For each application, the main motivation for using FL along with the associated challenges and detailed examples for use scenarios are given. Regarding the AI techniques, we consider MARL–based FL framework tailored to the needs of future wireless networks for ensuring fast convergence and high model accuracy of large state and action spaces. Particularly, to manage the fast varying radio channels and limited radio resources (e.g., transmission power and radio spectrum) in a cellular communication environment, this article proposes a robust MARL–based FL framework to enable local users to perform distributed power allocation, mode selection, resource allocation, and interference management. Finally, the paper outlines several prospective upcoming research topics, aimed to create constructive incorporation of MARL–based FL framework for future wireless networks.</p>

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Section: B Iot Applicationsmentioning

confidence: 99%

Federated Reinforcement Learning for 6G Wireless Networks: Fundamentals, Challenges and Future Research Trends

Das¹,

Mudi²,

Rahman³

et al. 2023

Preprint

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“…Multiple users work together to create a global learning model in FL, eliminating the requirement for data gathering and exchange with a central server. As a result, FL is a potential privacypreserving solution that has applications in a variety of technical fields and issues, including healthcare [44], intelligent radio access networks [44], IoT intrusion detection [45], and industrial IoT [46]. On the other hand, the traditional FL technique necessitates the presence of a central server, such as an MEC at the network edge [42].…”

Section: Federated Learningmentioning

confidence: 99%

Computing in the Sky: A Survey on Intelligent Ubiquitous Computing for UAV-Assisted 6G Networks and Industry 4.0/5.0

Alsamhi

Shvetsov

Kumar

et al. 2022

Drones

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Unmanned Aerial Vehicles (UAVs) are increasingly being used in a high-computation paradigm enabled with smart applications in the Beyond Fifth Generation (B5G) wireless communication networks. These networks have an avenue for generating a considerable amount of heterogeneous data by the expanding number of Internet of Things (IoT) devices in smart environments. However, storing and processing massive data with limited computational capability and energy availability at local nodes in the IoT network has been a significant difficulty, mainly when deploying Artificial Intelligence (AI) techniques to extract discriminatory information from the massive amount of data for different tasks.Therefore, Mobile Edge Computing (MEC) has evolved as a promising computing paradigm leveraged with efficient technology to improve the quality of services of edge devices and network performance better than cloud computing networks, addressing challenging problems of latency and computation-intensive offloading in a UAV-assisted framework. This paper provides a comprehensive review of intelligent UAV computing technology to enable 6G networks over smart environments. We highlight the utility of UAV computing and the critical role of Federated Learning (FL) in meeting the challenges related to energy, security, task offloading, and latency of IoT data in smart environments. We present the reader with an insight into UAV computing, advantages, applications, and challenges that can provide helpful guidance for future research.

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“…However, the detailed analysis and categorization of applications, security, architecture are not presented. Nguyen et al [28] surveyed FL in Internet-of-Medical-Things (IoMT) setups, and studied the requirements of reliable client-server communication requirements. Advanced FL mechanisms in IoMT are discussed, which addresses the computational constraints of IoMT setups.…”

Section: Existing Surveysmentioning

confidence: 99%

Adoption of Federated Learning for Healthcare Informatics: Emerging Applications and Future Directions

Patel¹,

Bhattacharya

Tanwar

et al. 2022

IEEE Access

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The smart healthcare system has improved the patients quality of life (QoL), where the records are being analyzed remotely by distributed stakeholders. It requires a voluminous exchange of data for disease prediction via the open communication channel, i.e., the Internet to train artificial intelligence (AI) models efficiently and effectively. The open nature of communication channels puts data privacy at high risk and affects the model training of collected data at centralized servers. To overcome this, an emerging concept, i.e., federated learning (FL) is a viable solution. It performs training at client nodes and aggregates their results to train the global model. The concept of local training preserves the privacy, confidentiality, and integrity of the patient's data which contributes effectively to the training process.The applicability of FL in the healthcare domain has various advantages, but it has not been explored to its extent. The existing surveys majorly focused on the role of FL in diverse applications, but there exists no detailed or comprehensive survey on FL in healthcare informatics (HI). We present a relative comparison of recent surveys with the proposed survey. To strengthen healthcare data privacy and increase the QoL of patients, we proposed an FL-based layered healthcare informatics architecture along with the case study on FLbased electronic health records (FL-EHR). We discuss the emerging FL models, and present the statistical and security challenges in FL adoption in medical setups. Thus, the review presents useful insights for both academia and healthcare practitioners to investigate FL application in HI ecosystems.

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Federated Learning for Smart Healthcare: A Survey

Cited by 337 publications

References 87 publications

Federated Reinforcement Learning for 6G Wireless Networks: Fundamentals, Challenges and Future Research Trends

Federated Reinforcement Learning for 6G Wireless Networks: Fundamentals, Challenges and Future Research Trends

Computing in the Sky: A Survey on Intelligent Ubiquitous Computing for UAV-Assisted 6G Networks and Industry 4.0/5.0

Adoption of Federated Learning for Healthcare Informatics: Emerging Applications and Future Directions

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