  Recently, Python Testbed for Federated Learning Algorithms emerged as a low
code and generative large language models amenable framework for developing
decentralized and distributed applications, primarily targeting edge systems,
by nonprofessional programmers with the help of emerging artificial
intelligence tools. This light framework is written in pure Python to be easy
to install and to fit into a small IoT memory. It supports formally verified
generic centralized and decentralized federated learning algorithms, as well as
the peer-to-peer data exchange used in time division multiplexing
communication, and its current main limitation is that all the application
instances can run only on a single PC. This paper presents the MicroPyton
Testbed for Federated Learning Algorithms, the new framework that overcomes its
predecessor's limitation such that individual application instances may run on
different network nodes like PCs and IoTs, primarily in edge systems. The new
framework carries on the pure Python ideal, is based on asynchronous I/O
abstractions, and runs on MicroPython, and therefore is a great match for IoTs
and devices in edge systems. The new framework was experimentally validated on
a wireless network comprising PCs and Raspberry Pi Pico W boards, by using
application examples originally developed for the predecessor framework.
