Assessment of patients with Parkinson’s disease based on federated learning

Guan, Bo; Yu, Lei; Li, Yang; Jia, Zhongwei; Jin, Zhen

doi:10.1007/s13042-023-01986-4

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2024

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“…Guan. explores the application of federated learning (FL) using wearable devices and real leg agility data from Parkinson's disease (PD) patients [20]. FL is a privacy-preserving approach that allows machine learning models to be trained without compromising the privacy of the underlying data.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Federated Learning Security, Scalability, and Future Incentives

2024

Adv Mach Lear Art Inte

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This study delves into the integration of Storj and blockchain technology within the context of federated learning (FL) and its implications for scalability and efficiency. By leveraging blockchain, we aimed to bolster security and transparency, while also addressing storage challenges through the integration of Incremental Learning. Our findings revealed that while the utilization of Storj led to marginally higher federated server storage requirements compared to local storage, particularly as the number of clients increased, there was also a slight increase in the time required for the federated learning process when Storj was integrated, especially with a larger number of clients.

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Section: Related Workmentioning

confidence: 99%

Enhancing Federated Learning Security, Scalability, and Future Incentives

2024

Adv Mach Lear Art Inte

View full text Add to dashboard Cite

show abstract

Assessment of patients with Parkinson’s disease based on federated learning

Cited by 1 publication

References 38 publications

Enhancing Federated Learning Security, Scalability, and Future Incentives

Enhancing Federated Learning Security, Scalability, and Future Incentives

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