Translating Federated Learning Algorithms in Python into CSP Processes Using ChatGPT

TL;DR

This paper presents an automated method using ChatGPT to translate Python federated learning algorithms into CSP processes, enabling automatic verification of their safety and liveness properties.

Contribution

It introduces a novel ChatGPT-based translation process that automates converting Python FL algorithms into CSP, simplifying verification workflows.

Findings

Successful translation of centralized FL algorithms verified by model checker

Effective estimation of minimal context using ChatGPT feedback

Automation reduces manual effort in formal verification of FL algorithms

Abstract

The Python Testbed for Federated Learning Algorithms is a simple Python FL framework that is easy to use by ML&AI developers who do not need to be professional programmers and is also amenable to LLMs. In the previous research, generic federated learning algorithms provided by this framework were manually translated into the CSP processes and algorithms' safety and liveness properties were automatically verified by the model checker PAT. In this paper, a simple translation process is introduced wherein the ChatGPT is used to automate the translation of the mentioned federated learning algorithms in Python into the corresponding CSP processes. Within the process, the minimality of the used context is estimated based on the feedback from ChatGPT. The proposed translation process was experimentally validated by successful translation (verified by the model checker PAT) of both generic centralized and decentralized federated learning algorithms.