  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.
