Can TensorFlow Federated be used in production setting, e.g., on mobile phones?
Currently not. Although we designed TFF with deployment to real devices in mind, at this stage we do not currently provide any tools for this purpose. The current release is intended for experimentation uses, such as expressing novel federated algorithms, or trying out federated learning with your own datasets, using the included simulation runtime.
We anticipate that over time the open source ecosystem around TFF will evolve to include runtimes targeting physical deployment platforms.
How do I use TFF to experiments with large datasets?
The default runtime included in the initial release of TFF is intended only for small experiments such as those described in our tutorials in which all your data (across all the simulated clients) simultaneously fits in memory on a single machine, and the entire experiment runs locally within the colab notebook.
Our near-term future roadmap includes a high-performance runtime for experiments with very large data sets and large numbers of clients.
How can I ensure randomness in TFF matches my expectations?
Since TFF has federated computing baked into its core, the writer of TFF should
not assume control over where and how TensorFlow Sessions are entered, or
run is called within those sessions. The semantics of randomness can depend on
entry and exit of TensorFlow Sessions if seeds are set. We recommend using
TensorFlow 2-style radomness, using for example
tf.random.experimental.Generator as of TF 1.14. This uses a tf.Variable to
manage its internal state.
To help manage expectations, TFF allows for the TensorFlow it serializes to have
op-level seeds set, but not graph-level seeds. This is because the semantics of
op-level seeds should be clearer in the TFF setting: a deterministic sequence
will be generated upon each invocation of a function wrapped as a
tf_computation, and only within this invocation will any guarantees made by
the pseudorandom number generator hold. Notice that this is not quite the same
as the semantics of calling a tf.function in eager mode; TFF effectively
enters and exits a unique tf.Session each time the tf_computation is
invoked, while repeatedly calling a function in eager mode is analogous to
calling sess.run on the output tensor repeatedly within the same session.
How can I contribute?
See the README, contributing guidelines, and collaborations.
What is the relationship between FedJAX and TensorFlow Federated?
TensorFlow Federated (TFF) is a full-fledged framework for federated learning and analytics 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 many privacy, compression, and optimization algorithms via its standard APIs. TFF also supports many types of FL research, with a collection of examples from published Google papers appearing in the google-research repo.
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.