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Blockchain-based Swarm Learning Framework in Personal Health Train

Thesis type
  • Master
Status Running
Supervisor(s)
Advisor(s)

Advanced analytics could power the data collected from numerous sources, both from healthcare institutions, or generated by individuals themselves via apps and devices, and lead to innovations in treatment and diagnosis of diseases; improve the care given to the patient, and empower citizens to participate in the decision-making process regarding their own health and well-being. With the world-widely emergence of data protection legislation, e.g., General Data Protection Regulation (GDPR) in the European Union, society is more aware of privacy. The sensitive nature of health data prohibits healthcare organizations from collecting and sharing the data. Traditional data analytics methods based on data centralization become less feasible. Distributed approaches shifting algorithms instead of data are solutions to comply with privacy protection regulations.

Swarm Learning is a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby guarantees high level of privacy protection.

The Personal Health Train (PHT) is a novel approach, aiming to establish a distributed data analytics infrastructure enabling the (re)use of distributed healthcare data, while data owners stay in control of their own data. The main principle of the PHT is that data remains in its original location, and analytical tasks visit data sources and execute the tasks. The PHT provides a distributed, flexible approach to use data in a network of participants, incorporating the FAIR principles. It facilitates the responsible use of sensitive and/or personal data by adopting international principles and regulations.

This Thesis focusses on the implementation of swarm learning on the top of PHT infrastructure; evaluation on the performance of distributed approaches, such as federated learning, gossip learning, and split learning.

If you are interested in this thesis, a related topic or have additional questions, please do not hesitate to send a message to mou@dbis.rwth-aachen.de

 

Prerequisites

Knowledge in programming languages, such as C++, Java, and Python will be fundamental.
In-depth knowledge in deep learning and familiar with deep learning frameworks such as PyTorch (recommended) and Keras.
Experience with cryptography and specific blockchain protocols, such as Hyperledger and Ethereum, will be very helpful.
Good communication skills.

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