I'm trying to use tensorflow federated to simulate a federated learning algorithm in a hierarchical topology. For what I've read, only a few strategies are implemented (such as tff.learning.build_federated_averaging_process), which relies on a single aggregation server. Is there any way to have networks that are more complex than the standard n-clients 1-server?
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TFF has native support for intermediate aggregation; a binary, for example, which runs code analogous to that implemented for testing here and serves analogously to the remote executor service could connect to workers and aggregate in a hierarchical manner.
You may, however, mean something more general than this.