At present many distributed and decentralized frameworks for federated learning algorithms are already available. However, development of such a framework targeting smart Internet of Things in edge systems is still an open challenge. A solution to that challenge named Python Testbed for Federated Learning Algorithms (PTB-FLA) appeared recently. This solution is written in pure Python, it supports both centralized and decentralized algorithms, and its usage was validated and illustrated by three simple algorithm examples. In this paper, we present the federated learning algorithms development paradigm based on PTB-FLA. The paradigm comprises the four phases named by the code they produce: (1) the sequential code, (2) the federated sequential code, (3) the federated sequential code with callbacks, and (4) the PTB-FLA code. The development paradigm is validated and illustrated in the case study on logistic regression, where both centralized and decentralized algorithms are developed.

翻译：当前虽已存在众多面向联邦学习算法的分布式与去中心化框架，但针对智能物联网边缘系统构建此类框架仍是一项开放性挑战。近期出现的名为"Python Testbed for Federated Learning Algorithms (PTB-FLA)"的解决方案，以纯Python语言编写，支持集中式与去中心化两种算法，其有效性已通过三个简单算法实例得到验证与展示。本文提出基于PTB-FLA的联邦学习算法开发范式，该范式包含四个阶段，各阶段以其产生的代码类型命名：(1) 顺序代码，(2) 联邦顺序代码，(3) 带回调函数的联邦顺序代码，以及(4) PTB-FLA代码。在逻辑回归案例研究中，该开发范式通过集中式与去中心化两种算法的并行开发过程得到验证与阐述。