Bayesian Inference Federated Learning for Heart Rate Prediction

Fang, Lei; Liu, Xiaoli; Su, Xiang; Ye, Juan; Dobson, Simon; Hui, Pan; Tarkoma, Sasu

doi:10.1007/978-3-030-70569-5_8

Cited by 11 publications

(14 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In addition, the authors implemented a regression model in [ 104 ] to predict heart rate using federated learning. They used Polar smartwatches to collect their own data, which were analyzed using FL sequential Bayesian and empirical Bayes-based hierarchical Bayesian models.…”

Section: Federated Learning In Actionmentioning

confidence: 99%

“…Moreover, most of these implementations are performed with publicly available data rather than using clinical or real-world data. For example, in the case of cardiovascular disease prediction, only [ 103 ] used real-world data from healthcare institutions and in the study in [ 104 ], real-world data from 10 individuals were used, whereas the others used either publicly available datasets or unspecified private data. In addition, none of these implementations were carried through to production readiness, but were conducted only as research studies.…”

Section: Federated Learning In Actionmentioning

confidence: 99%

“…For example, in the prediction of cardiovascular diseases, all of the studies listed in Table 4 were aimed at proving privacy-preservation concepts. In addition, the studies in [ 103 , 104 ] attempted to solve scalability problems using CVDs, while [ 108 ] attempted to solve personalization nodes using FL, and [ 110 ] addressed explainability, where reducing communication costs contributed to both privacy and personalization. In this context, only [ 109 ] addressed the disease itself, without targeting other FL-related topics, because it used a dataset from a clinical laboratory.…”

Section: Federated Learning In Actionmentioning

confidence: 99%

“…Despite the potential and usefulness of using smart wearables for disease detection using federated machine learning models, only one study ([ 104 ]) has employed a smart wearable to predict the onset of cardiovascular disease using data collected from a smartwatch for continuous, long-term, and real-time monitoring. In the other studies on cardiovascular disease, diabetes, or cancer, the use of smartwatches was not considered in the research.…”

Section: Federated Learning In Actionmentioning

confidence: 99%

See 3 more Smart Citations

Reviewing Federated Machine Learning and Its Use in Diseases Prediction

Moshawrab

Adda

Bouzouane

et al. 2023

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Federated Learning In Actionmentioning

confidence: 99%

Section: Federated Learning In Actionmentioning

confidence: 99%

Section: Federated Learning In Actionmentioning

confidence: 99%

Section: Federated Learning In Actionmentioning

confidence: 99%

See 2 more Smart Citations

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

“…Ref [86] To predict heart rate using an autoregression with an exogenous variable (ARX) model, Bayesian inference FL has been presented.…”

Section: Federated Data Sharing Architectures Fl On Ehr Datamentioning

confidence: 99%

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

Rauniyar¹,

Hagos²,

Jha³

et al. 2022

Preprint

View full text Add to dashboard Cite

With the advent of the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML)/Deep Learning (DL) algorithms, the data-driven medical application has emerged as a promising tool for designing reliable and scalable diagnostic and prognostic models from medical data. Hence, in recent years, data-driven medical applications have attracted a great deal of attention from academia to industry. This has undoubtedly improved the quality of healthcare delivery. However, AI-based medical applications still have poor adoption due to their difficulties in satisfying strict security, privacy, and quality of service standards such as low latency. Recent developments in Federated Learning (FL) have made it possible to train complex machine-learned models in a distributed manner and has become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, this survey paper highlights the current and future of FL technology in medical applications where data sharing is a significant burden. It also review and discuss the current research trends and their outcomes for designing reliable and scalable FL models. We outline the general FL's statistical problems, device challenges, security, privacy concerns, and its potential in the medical domain. Moreover, our study is also focused on medical applications where we highlight the burden of global cancer and the efficient use of FL for the development of computer-aided diagnosis tools for addressing them. Additionally, the recent literature revealed that FL models ensure a high level of robustness and generalization compared to the traditional data-driven medical applications. We hope that this review serves as a checkpoint that sets forth the existing state-of-the-art works in a thorough manner and offers open problems and future research directions for this field.

show abstract