Hi LazyLeopard18 ,
So long story short, yes it does.
Longer version, to really accomplish full federated learning with control over data at "compute points" you need some data abstraction layer. Without data abstraction layer, federated learning is just averaging derivatives from different location, this can be easily done with any distributed learning framework, such as horovod pr pytorch distributed or TF distributed.
If what you are after is, can I launch multiple experiments with the same code on remote machines with trains, the answer is Yes, this is exactly how trains-agent works, and it is very easy to setup on bare-metal (basically pip install). If you want full data abstraction, then this is missing from the open-source solution of Trains and only available in the paid tier I'm assuming that as a first step, you would like to achieve (1)?