Formal Verification of Trustworthy Federated Learning Systems

Abstract

Federated learning (FL) has emerged as a promising paradigm for training machine learning models on decentralized data, offering enhanced privacy and reduced communication costs. However, the inherent distributed nature of FL introduces significant challenges regarding trust, security, and model accuracy. This paper presents a formal verification framework for FL systems, leveraging secure multi-party computation (SMPC) and formal verification techniques to rigorously analyze data flow and model updates. The framework aims to provide guarantees about privacy, security, and model accuracy, addressing the unique vulnerabilities present in FL architectures. We define a mathematical model of an FL system, incorporating key elements such as clients, servers, and communication protocols. This model is then subjected to formal verification, utilizing techniques like model checking and symbolic execution to identify potential security breaches and inaccuracies. The results demonstrate the feasibility and effectiveness of applying formal verification to FL, offering a robust approach to ensuring the trustworthiness of these systems. Key performance metrics, including privacy loss, communication overhead, and model accuracy deviations, are quantified and analyzed within the verification process. The framework contributes to the development of more reliable and secure FL applications, particularly in sensitive domains such as healthcare and finance.