Published June 1, 2024 | Version v1
Book chapter Open

Chapter 4: Recent Advances in federated learning for digital healthcare systems

  • 1. Eurescom GmbH
  • 2. 6G Health Institute GmbH
  • 3. ROR icon Université de Rennes
  • 4. ROR icon Ericsson (Italy)

Description

This chapter covers the need & importance of federated learning in digital healthcare system. Federated learning in digital healthcare systems evolves around trust and without leaking of private data, thereby creating sustainability and resilience in these dynamic data ecosystems. It opens-up opportunities for novel research and business domains that can be looked at, as a prominent way to improve patient care globally. Federated learning can innovate the treatment cycle in more than one way, for example, to help finding similar patients, to accelerate drug discovery, or to decrease cost and time-to-market for pharma companies. The chapter covers how federated systems are perceived and what are the important considerations towards their development. The approach helps understanding the need for privacy preservation in a scalable and reliable way. Cutting-edge technologies and recent innovations are employed to oversee and disseminate patient information, aiding the implementation of novel patient-centric care models and services. These advancements have the potential to expedite the digital transformation of both society and the economy. 

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Additional details

Funding

European Commission
PAROMA-MED – Privacy Aware and Privacy Preserving Distributed and Robust Machine Learning for Medical Applications 101070222

Dates

Created
2024-07-05