PySyft is a privacy-preserving machine learning framework and remote computation engine. It functions as a decentralized data analysis orchestrator that allows for the execution of data science workflows on remote servers without requiring the transfer of raw private data from the host device.
The platform provides a secure collaboration environment where data owners manage permissions and authorize specific collaborators to run computations. It differentiates its workflow by utilizing mock data for local development and validation before submitting final analysis jobs to private remote servers.
The system covers a broad range of secure computation capabilities, including the use of sandboxed job execution to isolate computations from the underlying system and a cloud-storage transport layer for exchanging requests between peers. It also includes mechanisms for asynchronous state synchronization to maintain consistency across offline or cloud-connected nodes.