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Blockchain-Based Federated Learning in Medicine

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Artificial Intelligence in Medicine (AIME 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12299))

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Abstract

Worldwide epidemic events have confirmed the need for medical data processing tools while bringing issues of data privacy, transparency and usage consent to the front. Federated Learning and the blockchain are two technologies that tackle these challenges and have been shown to be beneficial in medical contexts where data are often distributed and coming from different sources. In this paper we propose to integrate these two technologies for the first time in a medical setting. In particular, we propose a implementation of a coordinating server for a federated learning algorithm to share information for improved predictions while ensuring data transparency and usage consent. We illustrate the approach with a prediction decision support tool applied to a diabetes data-set. The particular challenges of the medical contexts are detailed and a prototype implementation is presented to validate the solution.

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Notes

  1. 1.

    https://www.kaggle.com/ravichaubey1506/predictive-modelling-knn-ann-xgboost.

  2. 2.

    https://www.kaggle.com/uciml/pima-indians-diabetes-database.

  3. 3.

    https://github.com/n-vcs/solidity-fl.

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Acknowledgements

This work was supported by a grant from the Roche Institute 2018.

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Authors and Affiliations

  1. Institut de Recherche en Informatique de Toulouse SIG Team, Toulouse, France

    Omar El Rifai, Imen Megdiche, Franck Ravat & Olivier Teste

  2. ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, Toulouse, France

    Maelle Biotteau & Xavier de Boissezon

  3. Centre hospitalier universitaire de Toulouse, Toulouse, France

    Omar El Rifai, Maelle Biotteau & Xavier de Boissezon

Authors
  1. Omar El Rifai
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  2. Maelle Biotteau
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  3. Xavier de Boissezon
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  4. Imen Megdiche
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  5. Franck Ravat
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  6. Olivier Teste
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Corresponding author

Correspondence to Omar El Rifai .

Editor information

Editors and Affiliations

  1. School of Nursing, University of Minnesota, Minneapolis, MN, USA

    Martin Michalowski

  2. Ben-Gurion University of the Negev, Tonawanda, NY, USA

    Robert Moskovitch

Appendix A The Blockchain Technology: Key Concepts and Implementation

Appendix A The Blockchain Technology: Key Concepts and Implementation

This appendix explains some key concepts related to the blockchain as well as technical implementation details relevant to the present work.

1.1 A.1 Main Characteristics

The blockchain is a distributed ledger technology managed by a network of peers. Data on the blockchain are visible and duplicated across participants. New data are added to the blockchain through a consensus protocol. As such, the blockchain is decentralized, immutable and transparent by design.

1.2 A.2 Smart Contracts (SCs)

SCs are set of instructions, specified in digital form and executed when predefined conditions are met. Contrary to regular software, SCs benefit from the main characteristics of the blockchain and can help attain transparency and usage consent.

As opposed to centralized algorithms, SCs allow data owners to verify, at any time, the implementation of the FedAvg algorithm and replicate the results locally. Additionally, usage consent can be facilitated through SCs by logging data ownership certification directly on the blockchain as described in [16].

1.3 A.3 Consensus and Incentive Mechanism

A consensus protocol is at the heart of the blockchain’ mechanism. The Ethereum blockchain uses Proof-of-Work (PoW) by default. PoW relies on computational power to validate transactions or execute SCs. In our context, Medical institution are natural candidates for running the network as they will benefit from the resulting model. But to keep the network alive, nodes needs to be incentivized. As such, a possible set-up proposed in [30] is to have a reward mechanism for participants based on their data contribution.

1.4 A.4 Current Set-up

For the prototypeFootnote 3 an Ethereum blockchain is set-up on the back-end using the Truffle suite (Ganache and the Truffle Development Environment). Also, the FedAvg [20] algorithm and a basic ANN are developed using Python’s scientific computing library (Numpy). The front-end is developed in React. Data for each participant are stored locally in a MongoDB database.

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El Rifai, O., Biotteau, M., de Boissezon, X., Megdiche, I., Ravat, F., Teste, O. (2020). Blockchain-Based Federated Learning in Medicine. In: Michalowski, M., Moskovitch, R. (eds) Artificial Intelligence in Medicine. AIME 2020. Lecture Notes in Computer Science(), vol 12299. Springer, Cham. https://doi.org/10.1007/978-3-030-59137-3_20

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  • DOI: https://doi.org/10.1007/978-3-030-59137-3_20

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  • Online ISBN: 978-3-030-59137-3

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