Federated Learning for Smart Healthcare: A Survey

ACM Computing Surveys, Год журнала: 2022, Номер 55(3), С. 1 - 37

Опубликована: Фев. 3, 2022

Recent advances in communication technologies and the Internet-of-Medical-Things (IOMT) have transformed smart healthcare enabled by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection processing that may be infeasible realistic scenarios due to high scalability of modern networks growing privacy concerns. Federated Learning (FL), as an emerging distributed collaborative paradigm, is particularly attractive for healthcare, coordinating multiple clients (e.g., hospitals) perform training without sharing raw data. Accordingly, we provide a comprehensive survey on use FL healthcare. First, present recent FL, motivations, requirements using The designs are then discussed, ranging from resource-aware secure privacy-aware incentive personalized FL. Subsequently, state-of-the-art review applications key domains, including health management, remote monitoring, medical imaging, COVID-19 detection. Several FL-based projects analyzed, lessons learned also highlighted. Finally, discuss interesting research challenges possible directions future

Язык: Английский

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Federated Learning in Edge Computing: A Systematic Survey

Sensors, Год журнала: 2022, Номер 22(2), С. 450 - 450

Опубликована: Янв. 7, 2022

Edge Computing (EC) is a new architecture that extends Cloud (CC) services closer to data sources. EC combined with Deep Learning (DL) promising technology and widely used in several applications. However, conventional DL architectures enabled, producers must frequently send share third parties, edge or cloud servers, train their models. This often impractical due the high bandwidth requirements, legalization, privacy vulnerabilities. The Federated (FL) concept has recently emerged as solution for mitigating problems of unwanted loss, privacy, legalization. FL can co-train models across distributed clients, such mobile phones, automobiles, hospitals, more, through centralized server, while maintaining localization. therefore be viewed stimulating factor paradigm it enables collaborative learning model optimization. Although existing surveys have taken into account applications environments, there not been any systematic survey discussing implementation challenges paradigm. paper aims provide literature on environments taxonomy identify advanced solutions other open problems. In this survey, we review fundamentals FL, then related works EC. Furthermore, describe protocols, architecture, framework, hardware requirements environment. Moreover, discuss applications, challenges, FL. Finally, detail two relevant case studies applying EC, issues potential directions future research. We believe will help researchers better understand connection between enabling technologies concepts.

Язык: Английский

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Federated learning for smart cities: A comprehensive survey

Sustainable Energy Technologies and Assessments, Год журнала: 2022, Номер 55, С. 102987 - 102987

Опубликована: Дек. 31, 2022

Язык: Английский

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Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey

IEEE Access, Год журнала: 2021, Номер 9, С. 95730 - 95753

Опубликована: Янв. 1, 2021

The beginning of 2020 has seen the emergence coronavirus outbreak caused by a novel virus called SARS-CoV-2. sudden explosion and uncontrolled worldwide spread COVID-19 show limitations existing healthcare systems in timely handling public health emergencies. In such contexts, innovative technologies as blockchain Artificial Intelligence (AI) have emerged promising solutions for fighting epidemic. particular, can combat pandemics enabling early detection outbreaks, ensuring ordering medical data, reliable supply chain during tracing. Moreover, AI provides intelligent identifying symptoms treatments supporting drug manufacturing. Therefore, we present an extensive survey on use combating epidemics. First, introduce new conceptual architecture which integrates COVID-19. Then, latest research efforts various applications. newly emerging projects cases enabled these to deal with pandemic are also presented. A case study is provided using federated detection. Finally, point out challenges future directions that motivate more coronavirus-like

Язык: Английский

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A Systematic Review of Federated Learning in the Healthcare Area: From the Perspective of Data Properties and Applications

Applied Sciences, Год журнала: 2021, Номер 11(23), С. 11191 - 11191

Опубликована: Ноя. 25, 2021

Recent advances in deep learning have shown many successful stories smart healthcare applications with data-driven insight into improving clinical institutions’ quality of care. Excellent models are heavily data-driven. The more data trained, the robust and generalizable performance model. However, pooling medical centralized storage to train a model faces privacy, ownership, strict regulation challenges. Federated resolves previous challenges shared global using central aggregator server. At same time, patient remain local party, maintaining anonymity security. In this study, first, we provide comprehensive, up-to-date review research employing federated applications. Second, evaluate set recent from data-centric perspective learning, such as partitioning characteristics, distributions, protection mechanisms, benchmark datasets. Finally, point out several potential future directions

Язык: Английский

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Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

IEEE Internet of Things Journal, Год журнала: 2023, Номер 11(5), С. 7374 - 7398

Опубликована: Ноя. 1, 2023

With the advent of Internet Things (IoT), artificial intelligence (AI), machine learning (ML), and deep (DL) algorithms, landscape data-driven medical applications has emerged as a promising avenue for designing robust scalable diagnostic prognostic models from data. This gained lot attention both academia industry, leading to significant improvements in healthcare quality. However, adoption AI-driven still faces tough challenges, including meeting security, privacy, Quality-of-Service (QoS) standards. Recent developments federated (FL) have made it possible train complex machine-learned distributed manner become an active research domain, particularly processing data at edge network decentralized way preserve privacy address security concerns. To this end, article, we explore present future FL technology where sharing is challenge. We delve into current trends their outcomes, unraveling complexities reliable models. article outlines fundamental statistical issues FL, tackles device-related problems, addresses navigates complexity concerns, all while highlighting its transformative potential field. Our study primarily focuses on context global cancer diagnosis. highlight enable computer-aided diagnosis tools that challenge with greater effectiveness than traditional methods. literature shown are generalize well new data, which essential applications. hope comprehensive review will serve checkpoint field, summarizing state art identifying open problems directions.

Язык: Английский

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Federated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review

Knowledge-Based Systems, Год журнала: 2023, Номер 274, С. 110658 - 110658

Опубликована: Май 22, 2023

Recent developments in the Internet of Things (IoT) and various communication technologies have reshaped numerous application areas. Nowadays, IoT is assimilated into medical devices equipment, leading to progression Medical (IoMT). Therefore, IoMT-based healthcare applications are deployed used day-to-day scenario. Traditionally, machine learning (ML) models use centralized data compilation that impractical pragmatic frameworks due rising privacy security issues. Federated Learning (FL) has been observed as a developing distributed collective paradigm, most appropriate for modern framework, manages stakeholders (e.g., patients, hospitals, laboratories, etc.) carry out training without actual exchange sensitive data. Consequently, this work, authors present an exhaustive survey on FL-based IoMT smart frameworks. First, introduced devices, their types, applications, datasets, framework detail. Subsequently, concept FL, its domains, tools develop FL discussed. The significant contribution deploying secure systems presented by focusing patents, real-world projects, datasets. A comparison techniques with other schemes ecosystem also presented. Finally, discussed challenges faced potential future research recommendations deploy

Язык: Английский

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Distributed Anomaly Detection in Smart Grids: A Federated Learning-Based Approach

IEEE Access, Год журнала: 2023, Номер 11, С. 7157 - 7179

Опубликована: Янв. 1, 2023

The smart grid integrates Information and Communication Technologies (ICT) into the traditional power to manage generation, distribution, consumption of electrical energy. Despite its many advantages, it faces significant challenges, such as detecting abnormal behaviours in grid. Identifying anomalous helps discover unusual user consumption, faulty infrastructure, outages, equipment failures, energy thefts, or cyberattacks. Machine learning (ML)-based techniques on meter data has shown remarkable results anomaly detection. However, ML-based detection requires meters share local with a central server, which raises concerns regarding security privacy. Server-based model training additional requirement centralised computing power, reliable network communication, large bandwidth capacity, latency issues, all affect real-time performance. Motivated by these concerns, we propose Federated Learning (FL)-based scheme where ML models are trained locally without sharing thus ensuring In proposed approach, global is downloaded from server for on-device training. After training, parameters sent improve model. We secure parameter updates adversaries using SSL/TLS protocol. Using standard datasets, investigate performance federated observe that FL achieve comparable while Further, our study shows FL-based perform efficiently terms memory, CPU usage, at edge devices suitable implementation resource-constrained environments, meters,

Язык: Английский

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Federated learning for medical image analysis: A survey

Pattern Recognition, Год журнала: 2024, Номер 151, С. 110424 - 110424

Опубликована: Март 12, 2024

Язык: Английский

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Review on security of federated learning and its application in healthcare

Future Generation Computer Systems, Год журнала: 2023, Номер 144, С. 271 - 290

Опубликована: Фев. 25, 2023

Язык: Английский

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