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Translating Federated Learning Algorithms in Python into CSP Processes Using ChatGPT

Created by
  • Haebom

Author

Miroslav Popovic, Marko Popovic, Miodrag Djukic, Ilija Basicevic

Outline

This paper introduces a Python-based federated learning framework and presents a method for automatically converting federated learning algorithms built on the framework into Communicating Sequential Processes (CSPs) using ChatGPT. While manual conversion was previously required, ChatGPT automates the process, increasing efficiency. The converted CSP processes are then verified for safety and liveness using the PAT model verifier. Experiments demonstrate successful conversion and validation for both centralized and distributed federated learning algorithms.

Takeaways, Limitations

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Takeaways:
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By presenting a novel method that automatically converts federated learning algorithms into CSP processes using ChatGPT, we can streamline the validation process of federated learning algorithms.
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We provide a Python-based federated learning framework that is easy to use even for non-professional programmers.
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We present a new possibility of utilizing LLM (Large Language Model) to verify federated learning algorithms.
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Limitations:
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Because it is highly dependent on ChatGPT's response, the accuracy of the conversion results may vary depending on ChatGPT's performance.
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Currently, it is only applicable to certain Python-based federated learning frameworks, and its extensibility to other frameworks requires further research.
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The process of estimating the minimality of code generated by ChatGPT can be subjective, and more objective evaluation criteria are needed.
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