# Decision Models for Selecting Federated Learning Architecture Patterns

arXiv: https://arxiv.org/abs/2204.13291 · HTML: https://mlsanity.com/paper/2204.13291/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 37 references that are in the map.
- First posted on arXiv 28 Apr 2022.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data](https://mlsanity.com/paper/1811.11479/) (2018)
- [Client-Edge-Cloud Hierarchical Federated Learning](https://mlsanity.com/paper/1905.06641/) (2019)
- [BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning](https://mlsanity.com/paper/1905.06731/) (2019)
- [OpenFL: An open-source framework for Federated Learning](https://mlsanity.com/paper/2105.06413/) (2021)
- [Wireless Federated Distillation for Distributed Edge Learning with Heterogeneous Data](https://mlsanity.com/paper/1907.02745/) (2019)
- [Privacy-Preserving Asynchronous Federated Learning Algorithms for Multi-Party Vertically Collaborative Learning](https://mlsanity.com/paper/2008.06233/) (2020)
- [Architectural Patterns for the Design of Federated Learning Systems](https://mlsanity.com/paper/2101.02373/) (2021)
- [A Systematic Literature Review on Federated Machine Learning: From A Software Engineering Perspective](https://mlsanity.com/paper/2007.11354/) (2020)
- [FLRA: A Reference Architecture for Federated Learning Systems](https://mlsanity.com/paper/2106.11570/) (2021)
- [Industrial Federated Learning -- Requirements and System Design](https://mlsanity.com/paper/2005.06850/) (2020)

## Abstract (arXiv)

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.