Medical data generated at Hospitals and Research centers cannot leave its place of origin due to privacy norms. With Edge Computing and a decentralised approach, the data will never leave the device and it utilises the processing power of the individual edge devices. For this, we utilize open source Swarm Learning.
Swarm Learning is a decentralized, privacy-preserving Machine Learning framework. This framework utilizes the computing power at, or near, the distributed data sources to run the Machine Learning algorithms that train the models. It uses the security of a blockchain platform to share learnings with peers in a safe and secure manner. In Swarm Learning, training of the model occurs at the edge, where data is most recent, and where prompt, data-driven decisions are mostly necessary. In this completely decentralized architecture, only the insights learned are shared with the collaborating ML peers, not the raw data. This tremendously enhances data security and privacy.
This model was swarmified by using the following architecture - An SN Node, two SL Nodes on seperate hosts for training. For more information on how to run the model, see CIFAR10 example.