First public release of FFL - Fast Federated Learning. This release contains three different examples of use of FFL for Federated Learning (FL) and Edge inference (EI): Master-worker (FL) Peer-to-peer (FL) Tree-based (EI) The setup.sh script downloads and installs all the necessary software for running the experiments (apart from GCC, make, unzip, and OpenCV, which are on the user). The reproduce.sh script is designed to reproduce the full suite of experiments reported in the "Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning", published at the ACM Computing Frontiers 2023 conference. Full Changelog: https://github.com/alpha-unito/FastFederatedLearning/commits/v0.1.0-alpha