# Correct orchestration of Federated Learning generic algorithms: formalisation and verification in CSP

arXiv: https://arxiv.org/abs/2306.14529 · HTML: https://mlsanity.com/paper/2306.14529/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2023.

## Built on by (most cited first, 2 of 2)

- [Federated Isolation Forest for Efficient Anomaly Detection on Edge IoT Systems](https://mlsanity.com/paper/2506.05138/) (2025)
- [Translating Federated Learning Algorithms in Python into CSP Processes Using ChatGPT](https://mlsanity.com/paper/2506.07173/) (2025)

## Abstract (arXiv)

Federated learning (FL) is a machine learning setting where clients keep the training data decentralised and collaboratively train a model either under the coordination of a central server (centralised FL) or in a peer-to-peer network (decentralised FL). Correct orchestration is one of the main challenges. In this paper, we formally verify the correctness of two generic FL algorithms, a centralised and a decentralised one, using the CSP process calculus and the PAT model checker. The CSP models consist of CSP processes corresponding to generic FL algorithm instances. PAT automatically proves the correctness of the two generic FL algorithms by proving their deadlock freeness (safety property) and successful termination (liveness property). The CSP models are constructed bottom-up by hand as a faithful representation of the real Python code and is automatically checked top-down by PAT.