Interaction Patterns for Regulatory Compliance in Federated Learning

abstract

Organizations in highly regulated domains often struggle to build well-performing machine learning (ML) models due to restrictions from data protection regulation. Federated learning (FL) has recently been introduced as a potential remedy, whereby organizations share local models while keeping data on premise. Still, regulatory compliance remains challenging in FL settings: training data needs to be shared to some extent, and models can be reverse engineered or misused towards violation of data privacy by each participating organization. Guided by design science methodology, we introduce four interaction patterns that allow for compliance-by-design and trust-context-sensitive analysis of an FL system by combining different approaches to privacy preservation. We match the patterns to privacy principles and exemplify how verifiable claims about compliance at design- and operation-time FL can be generated to make all participating organizations accountable.