Decision Models for Selecting Federated Learning Architecture Patterns

Federated learning is growing fast in academia and industries as a solution to solve data hungriness and privacy issues in machine learning. Being a widely distributed system, federated learning requires various system design thinking. To better design a federated learning system, researchers have introduced multiple patterns and tactics that cover various system design aspects. However, the multitude of patterns leaves the designers confused about when and which pattern to adopt. In this paper, we present a set of decision models for the selection of patterns for federated learning architecture design based on a systematic literature review on federated learning, to assist designers and architects who have limited knowledge of federated learning. Each decision model maps functional and non-functional requirements of federated learning systems to a set of patterns. We also clarify the trade-offs in the patterns. We evaluated the decision models by mapping the decision patterns to concrete federated learning architectures by big tech firms to assess the models' correctness and usefulness. The evaluation results indicate that the proposed decision models are able to bring structure to the federated learning architecture design process and help explicitly articulate the design rationale.