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

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