Decision Models for Selecting Federated Learning Architecture Patterns
- URL: http://arxiv.org/abs/2204.13291v3
- Date: Fri, 28 Apr 2023 00:30:38 GMT
- Title: Decision Models for Selecting Federated Learning Architecture Patterns
- Authors: Sin Kit Lo, Qinghua Lu, Hye-Young Paik, Liming Zhu
- Abstract summary: We present a set of decision models for the selection of patterns for federated machine learning architecture design.
Each decision model maps functional and non-functional requirements of federated machine learning systems to a set of patterns.
- Score: 7.468413169676602
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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