Swarm Learning for decentralized and confidential clinical machine learning

By S. Warnat-Herresthal, Hartmut Schultze, Krishnaprasad Shastry, Sathyanarayanan Manamohan, Saikat Mukherjee, Vishesh Garg, Ravi Sarveswara, K. Händler, P. Pickkers, N. Aziz and 55 more. Nature, 2021

792 citations recorded by the source corpus.

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—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 going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine. Swarm Learning is a decentralized machine learning approach that outperforms classifiers developed at individual sites for COVID-19 and other diseases while preserving confidentiality and privacy.

Subfields: Artificial Intelligence, Health Informatics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/24d21ecaeb2d2ecc20e26a5e3f5128247704ccfe

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Citations in the corpus

Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, background): “to-peer network was developed in [35] to support swarm learning without a central server.”
Artificial intelligence in histopathology: enhancing cancer research and clinical oncology (2022, background): “Similarly, in swarm learning 124,125 , multiple parties co-train a model, but, in this case, they are coordinated by blockchain-based communication.”
Multimodal biomedical AI (2022, background): “Finally, swarm learning is a relatively novel approach that, similarly to federated learning, is also based on several individuals or organizations training a model on local data, but does not require a trusted central server because it replaces it with the use of blockchain smart contracts 170 .”
Privacy and Robustness in Federated Learning: Attacks and Defenses (2020, background): “The recent emerging swarm learning [187] can be deemed as a decentralized FL framework, which unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator.”
Decentralized Federated Learning: A Survey and Perspective (2023, background): “Another example of a star variant is the Swarm Learning framework proposed by Warnat-Herresthal et al. [53], which involves the dynamic election of a leader to aggregate model parameters.”
Communication-efficient federated learning via knowledge distillation (2021, positive): “Thus, it can be dif ﬁ cult to collect and aggregate decentralized data into a uni ﬁ ed dataset for centralized model training, which poses great challenges to the applications of many existing deep learning techniques without privacy awareness 8 .”
Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare (2022, background): “The advantage of this option is that it minimizes the burden on the AI-QI team and the model vendor can leverage data from multiple institutions to improve model monitoring and maintenance 97,98 .”
Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges (2022, background): “[158] 2021 Facilitate the integration of any medical data from any data owner worldwide without violating privacy laws Cross-silo Decentralized Real Custom model Custom aggregator Asynchronous Scheduling PBMC transcriptome [159] Chest X-ray [160]”