Formal Model of Trust in Federated Learning Systems

Abstract

Federated Learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing. However, the inherent trust relationships within FL systems are complex and often overlooked. This paper presents a formal model for analyzing trust dynamics in FL, incorporating uncertainties in data and model updates. We leverage game theory and Bayesian networks to represent the interactions between participants, considering factors such as data quality, model accuracy, and communication security. The model allows us to formally quantify trust levels and explore strategies for building robust and reliable FL systems. The key contributions of this work lie in providing a rigorous mathematical framework for understanding trust in FL, which can be used to design mechanisms for incentivizing participation, mitigating malicious behavior, and ultimately, enhancing the overall performance and security of FL systems. The model incorporates probabilistic elements represented through conditional probability tables and utility functions, allowing for a nuanced assessment of trust based on various contributing factors. ---