See the README and guidelines for contributors.
Unfortunately, no. FedJAX is a lightweight Python- and JAX-based simulation library that focuses on ease of use and rapid prototyping of federated learning algorithms for research purposes.
TensorFlow Federated (TFF) is a full-fledged framework for federated learning that is designed to facilitate composing different algorithms and features, and to enable porting code across different simulation and deployment scenarios. TFF provides a scalable runtime and supports several privacy, compression algorithms, and optimizers, as outlined in https://www.tensorflow.org/federated/tff_for_research. In contrast, FedJAX is a lightweight Python- and JAX-based simulation library that focuses on ease of use and rapid prototyping of federated learning algorithms for research purposes. TensorFlow Federated and FedJAX are developed as separate projects, without expectation of code portability.