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：目前，已有多种适用于联邦学习算法的分布式和去中心化框架可用。然而，针对边缘系统中智能物联网场景开发此类框架仍是一个开放挑战。近期出现了一种名为"联邦学习算法Python测试平台（PTB-FLA）"的解决方案。该方案采用纯Python编写，支持中心化和去中心化算法，并通过三个简单算法示例验证和展示了其用法。本文提出了基于PTB-FLA的联邦学习算法开发范式。该范式包含四个阶段，以其生成的代码命名：（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。本文通过逻辑回归案例研究对该开发范式进行了验证和说明，其中分别开发了中心化算法和去中心化算法。