Decision Models for Selecting Federated Learning Architecture Patterns

TL;DR

This paper introduces decision models to help system designers select appropriate architecture patterns for federated machine learning, simplifying complex choices and clarifying design rationale.

Contribution

It presents systematic decision models based on literature review that map requirements to patterns, aiding architects with limited federated learning knowledge.

Findings

Decision models effectively structure federated learning architecture design.

Models help articulate clear design rationale.

Evaluation shows models' correctness and usefulness.

Abstract

Federated machine 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 machine learning requires various system design thinking. To better design a federated machine 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 machine learning architecture design based on a systematic literature review on federated machine learning, to assist designers and architects who have limited knowledge of federated machine learning. Each decision model maps functional and non-functional requirements of federated machine learning systems to a set of patterns. We also clarify the drawbacks of the patterns. We evaluated the decision models by mapping the decision patterns to concrete federated machine 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 machine learning architecture design process and help explicitly articulate the design rationale.