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Federated learning has received fast-growing interests from academia andindustry to tackle the challenges of data hungriness and privacy in machinelearning. A federated learning system can be viewed as a large-scaledistributed system with different components and stakeholders as numerousclient devices participate in federated learning. Designing a federatedlearning system requires software system design thinking apart from machinelearning knowledge. Although much effort has been put into federated learningfrom the machine learning technique aspects, the software architecture designconcerns in building federated learning systems have been largely ignored.Therefore, in this paper, we present a collection of architectural patterns todeal with the design challenges of federated learning systems. Architecturalpatterns present reusable solutions to a commonly occurring problem within agiven context during software architecture design. The presented patterns arebased on the results of a systematic literature review and include three clientmanagement patterns, four model management patterns, three model trainingpatterns, and four model aggregation patterns. The patterns are associated tothe particular state transitions in a federated learning model lifecycle,serving as a guidance for effective use of the patterns in the design offederated learning systems.

Architectural Patterns for the Design of Federated Learning Systems