Translating Federated Learning Algorithms in Python into CSP Processes Using ChatGPT (2506.07173v1)

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.

• The paper presents a translation methodology that automates converting Python-based federated learning algorithms into CSP processes using ChatGPT.
• It details an iterative process involving context creation, automated translation, and verification via the PAT model checker with minimal manual correction.
• The study highlights successes in centralized FL translations and increased challenges in decentralized cases, paving the way for future LLM improvements.

Automating the Translation of Federated Learning Algorithms into CSP Processes Using ChatGPT

The paper "Translating Federated Learning Algorithms in Python into CSP Processes Using ChatGPT" explores the use of ChatGPT as a tool for automating the translation of Python-based federated learning algorithms into Communicating Sequential Processes (CSP) models. This research is situated within the EU Horizon 2020 project TaRDIS, which focuses on developing a toolbox for AI/ML-powered distributed applications, aiming for safety-critical, correct-by-construction systems through formal verification methods.

The Python Testbed for Federated Learning Algorithms (PTB-FLA) is an FL framework that supports both centralized and decentralized federated learning (FL) orchestration protocols. Previously, translations of these algorithms into CSP#—a dialect of CSP used with the PAT model checker—were done manually, imposing a significant limitation due to the labor-intensive nature of this approach. This paper attempts to streamline that process by leveraging the capabilities of a LLM, specifically ChatGPT, to reduce human involvement.

Methodology

The proposed translation methodology is an iterative process involving:

1. Context Specification: A prompt is crafted to guide ChatGPT in the translation task.
2. ChatGPT Execution: ChatGPT completes the translation based on the provided context.
3. Verification via PAT: The output CSP# code is tested with the PAT model checker for syntactic correctness and property validation.
4. Error Correction Loop: If the CSP# code is incorrect, either minor modifications are manually applied, or the context is adjusted to refine the translation further.

The paper emphasizes economical context creation to minimize ChatGPT usage costs. Post-translation, ChatGPT provides feedback on task difficulty, critical context elements, and redundant information to refine the process.

Results

The experimental validation demonstrates the effectiveness of this automated translation method on both centralized and decentralized FL algorithms. For the centralized FL algorithm, the translation was successfully completed with minor manual intervention, which primarily involved syntax corrections. ChatGPT evaluated the translation difficulty as moderately challenging, underscoring the utility of MPAPI description in facilitating accurate translation.

In the decentralized FL scenario, the translation required four iterations, revealing more complexity compared to the centralized case. ChatGPT committed syntax errors such as incorrect use of symbols and logical errors that involved mismanagement of process calls and iterative logic. These issues highlight the current limitations of LLMs in maintaining logical consistency across more complex algorithmic structures. The translation difficulty was rated higher in this case, with the treatment of message-passing functions deemed particularly crucial.

Implications and Future Directions

This research offers practical implications for automating the translation of programming languages into formal verification models, potentially transforming workflows in AI system development where formal verification is crucial. However, reliance on manual error correction indicates a need for improving the error-handling capabilities of LLMs like ChatGPT.

Future research should focus on reducing error rates in such translations, potentially through dataset creation or iterative feedback mechanisms integrated into LLMs, as indicated by avenues like IterGen. An exploration of open LLMs for this purpose may also be valuable to extend the capabilities beyond the current models.

Overall, this paper provides a preliminary pathway for integrating AI tools into the domain of formal verification, aiming to increase efficiency and lower the barrier for developers utilizing model-checking frameworks like CSP# and PAT in federated learning environments.