A Federated Learning Algorithms Development Paradigm

TL;DR

This paper introduces a development paradigm for federated learning algorithms using the PTB-FLA framework, facilitating the creation of centralized and decentralized algorithms in edge IoT systems.

Contribution

It proposes a four-phase development paradigm for federated learning algorithms based on the PTB-FLA Python testbed, validated through a logistic regression case study.

Findings

The paradigm effectively supports the development of federated algorithms.

Validation with logistic regression demonstrates its practical applicability.

Supports both centralized and decentralized federated learning algorithms.

Abstract

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.