The future of digital health with federated learning
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Data-driven machine learning (ML) has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems. Existing medical data is not fully exploited by ML primarily because it sits in data silos and privacy concerns restrict access to this data. However, without access to sufficient data, ML will be prevented from reaching its full potential and, ultimately, from making the transition from research to clinical practice. This paper considers key factors contributing to this issue, explores how federated learning (FL) may provide a solution for the future of digital health and highlights the challenges and considerations that need to be addressed.
What this paper cites, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| Deep Learning with Differential Privacy | 2016 | 6,220 |
| Federated Learning: Challenges, Methods, and Future Directions | 2020 | 4,992 |
| Membership Inference Attacks Against Machine Learning Models | 2017 | 4,490 |
| Privacy-Preserving Deep Learning | 2015 | 2,325 |
What cites it, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| Federated Learning for Internet of Things: A Comprehensive Survey | 2021 | 1,465 |
Links
Topics
| Machine Learning in Healthcare | Computer Science |
| Privacy-Preserving Technologies in Data | Computer Science |
| Artificial Intelligence in Healthcare and Education | Medicine |
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