“TensorFlow Federated (TFF) is an open-source framework for machine learning and other computations on decentralized data. TFF has been developed to facilitate

“TensorFlow Federated (TFF) is an open-source framework for machine learning and other computations on decentralized data. TFF has been developed to facilitate open research and experimentation with Federated Learning (FL), an approach to machine learning where a shared global model is trained across many participating clients that keep their training data locally. For example, FL has been used to train prediction models for mobile keyboards without uploading sensitive typing data to servers.“ From https://www.tensorflow.org/federated https://www.tensorflow.org/federated This is a very exiciting project that compliments other privacy-preserving machine learning techniques (PPML) in TensorFlow: - Differential Privacy: https://github.com/tensorflow/privacy https://github.com/tensorflow/privacy - Secure Multi-Party Computation: https://github.com/mortendahl/tf-encrypted https://github.com/mortendahl/tf-encrypted - Confidential Computing (Trusted Execution Environments, Asylo, Intel SGX): https://github.com/dropoutlabs/tf-trusted https://github.com/dropoutlabs/tf-trusted