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.
Citation edge evidence
- Federated Learning for Internet of Things: A Comprehensive Survey→The future of digital health with federated learningLegacy edge — source detail unavailable
- The future of digital health with federated learning→Deep Learning with Differential PrivacyLegacy edge — source detail unavailable
- The future of digital health with federated learning→Federated Learning: Challenges, Methods, and Future DirectionsLegacy edge — source detail unavailable
- The future of digital health with federated learning→Membership Inference Attacks Against Machine Learning ModelsLegacy edge — source detail unavailable
- The future of digital health with federated learning→Privacy-Preserving Deep LearningLegacy edge — source detail unavailable
- Deep Learning with Differential Privacy→Privacy-Preserving Deep LearningLegacy edge — source detail unavailable
- Membership Inference Attacks Against Machine Learning Models→Privacy-Preserving Deep LearningLegacy edge — source detail unavailable
- Membership Inference Attacks Against Machine Learning Models→Deep Learning with Differential PrivacyLegacy edge — source detail unavailable
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
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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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- supportsA title is present.
Provenance
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