FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare

Chen, Yiqiang; Qin, Xin; Wang, Jindong; Yu, Chaohui; Gao, Wen

doi:10.1109/mis.2020.2988604

Cited by 650 publications

(377 citation statements)

References 13 publications

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“…(2018) adopted FL to train a predictive model and solve a support vector machine problem for analysis of electronic health record (EHR) data. FL was applied for wearable healthcare using personalized machine learning models ( Chen et al., 2020 ). Li et al.…”

Section: Related Workmentioning

confidence: 99%

Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan

Yang

et al. 2021

Medical Image Analysis

199

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Section: Related Workmentioning

confidence: 99%

Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan

Yang

et al. 2021

Medical Image Analysis

199

130

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“…It would also be interesting to evaluate the performance of other machine learning approaches, such as deep learning. Perhaps a method of transfer learning similar to that contemplated in [45] might be achievable, and would be worthy of exploration.…”

Section: Conclusion and Future Remarksmentioning

confidence: 99%

IoT Network Security: Threats, Risks, and a Data-Driven Defense Framework

Wheelus

Zhu

2020

IoT

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The recent surge in Internet of Things (IoT) deployment has increased the pace of integration and extended the reach of the Internet from computers, tablets and phones to a myriad of devices in our physical world. Driven by the IoT, with each passing day, the Internet becomes more integrated with everyday life. While IoT devices provide endless new capabilities and make life more convenient, they also vastly increase the opportunity for nefarious individuals, criminal organizations and even state actors to spy on, and interfere with, unsuspecting users of IoT systems. As this looming crisis continues to grow, calls for data science approaches to address these problems have increased, and current research shows that predictive models trained with machine learning algorithms hold great potential to mitigate some of these issues. In this paper, we first carry out an analytics approach to review security risks associated with IoT systems, and then propose a machine learning-based solution to characterize and detect IoT attacks. We use a real-world IoT system with secured gate access as a platform, and introduce the IoT system in detail, including features to capture security threats/attacks to the system. By using data collected from a nine month period as our testbed, we evaluate the efficacy of predictive models trained by means of machine learning, and propose design principles and a loose framework for implementing secure IoT systems.

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“…• Ensuring data integrity when communicating • Designing secure encryption methods that take full advantage of the computational resources • Use devices to reduce idle time One example of an FL architecture geared towards a specific industry comes from [49]. In the article, the authors propose an FL framework centering around healthcare, specifically wearable devices, which fall into the category of Smart Healthcare.…”

Section: Figure 8 Framework Of Perfitmentioning

confidence: 99%

Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications

et al. 2020

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This paper provides a comprehensive study of Federated Learning (FL) with an emphasis on enabling software and hardware platforms, protocols, real-life applications and use-cases. FL can be applicable to multiple domains but applying it to different industries has its own set of obstacles. FL is known as collaborative learning, where algorithm(s) get trained across multiple devices or servers with decentralized data samples without having to exchange the actual data. This approach is radically different from other more established techniques such as getting the data samples uploaded to servers or having data in some form of distributed infrastructure. FL on the other hand generates more robust models without sharing data, leading to privacy-preserved solutions with higher security and access privileges to data. This paper starts by providing an overview of FL. Then, it gives an overview of technical details that pertain to FL enabling technologies, protocols, and applications. Compared to other survey papers in the field, our objective is to provide a more thorough summary of the most relevant protocols, platforms, and real-life use-cases of FL to enable data scientists to build better privacy-preserving solutions for industries in critical need of FL. We also provide an overview of key challenges presented in the recent literature and provide a summary of related research work. Moreover, we explore both the challenges and advantages of FL and present detailed service use-cases to illustrate how different architectures and protocols that use FL can fit together to deliver desired results.

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FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare

Cited by 650 publications

References 13 publications

Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan

Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan

IoT Network Security: Threats, Risks, and a Data-Driven Defense Framework

Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications

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