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.

翻译：最近，Python联邦学习算法测试床作为一种低代码且可生成大语言模型的框架，使非专业程序员能够在新兴人工智能工具的辅助下开发去中心化和分布式应用，主要面向边缘系统。该轻量级框架采用纯Python编写，易于安装且适配小型物联网内存。它支持经过形式化验证的通用集中式和去中心化联邦学习算法，以及时分复用通信中使用的点对点数据交换，而其当前的主要局限性在于所有应用实例仅能在单台PC上运行。本文介绍了MicroPython联邦学习算法测试床这一新框架，它突破了前代框架的局限性，使得单个应用实例可在不同网络节点（如PC和物联网设备）上运行，主要应用于边缘系统。新框架延续了纯Python理念，基于异步I/O抽象构建，并运行在MicroPython上，因此非常适合边缘系统中的物联网设备及装置。通过使用前代框架开发的应用实例，该新框架在包含PC和树莓派Pico W板的无线网络上进行了实验验证。