What are the libraries we can use for federated learning process? what are the data encryption techniques used to protect datasets?
I studied some articles and blogs but in health sector it's difficult to implement because health organisations are not agree t share data. in other way they have no trust on federated learning that it would support as a protective way
There are several libraries available for implementing federated learning, including TensorFlow Federated, PySyft, and Flower. These libraries provide tools for defining federated learning workflows and orchestrating the communication between the central server and the hospitals.
To protect the privacy of patient data during federated learning, data encryption techniques such as homomorphic encryption and secure multi-party computation can be used. Homomorphic encryption allows computation to be performed directly on encrypted data without needing to decrypt it first, while secure multi-party computation allows multiple parties to jointly compute a function on their private inputs without revealing them to each other. These techniques can be used to ensure that patient data remains encrypted throughout the federated learning process.
