  Decentralised Machine Learning (DML) enables collaborative machine learning
without centralised input data. Federated Learning (FL) and Edge Inference are
examples of DML. While tools for DML (especially FL) are starting to flourish,
many are not flexible and portable enough to experiment with novel processors
(e.g., RISC-V), non-fully connected network topologies, and asynchronous
collaboration schemes. We overcome these limitations via a domain-specific
language allowing us to map DML schemes to an underlying middleware, i.e. the
FastFlow parallel programming library. We experiment with it by generating
different working DML schemes on x86-64 and ARM platforms and an emerging
RISC-V one. We characterise the performance and energy efficiency of the
presented schemes and systems. As a byproduct, we introduce a RISC-V porting of
the PyTorch framework, the first publicly available to our knowledge.
