# A Systematic Literature Review on Federated Machine Learning: From A Software Engineering Perspective

arXiv: https://arxiv.org/abs/2007.11354 · HTML: https://mlsanity.com/paper/2007.11354/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2020.

## Built on by (most cited first, 14 of 14)

- [Federated Learning for Computationally-Constrained Heterogeneous Devices: A Survey](https://mlsanity.com/paper/2307.09182/) (2023)
- [Phoenix: A Federated Generative Diffusion Model](https://mlsanity.com/paper/2306.04098/) (2023)
- [Architectural Patterns for the Design of Federated Learning Systems](https://mlsanity.com/paper/2101.02373/) (2021)
- [Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directions](https://mlsanity.com/paper/2207.09611/) (2022)
- [The Pipeline for the Continuous Development of Artificial Intelligence Models -- Current State of Research and Practice](https://mlsanity.com/paper/2301.09001/) (2023)
- [Trustworthy Federated Learning: Privacy, Security, and Beyond](https://mlsanity.com/paper/2411.01583/) (2024)
- [FLRA: A Reference Architecture for Federated Learning Systems](https://mlsanity.com/paper/2106.11570/) (2021)
- [FederatedTrust: A Solution for Trustworthy Federated Learning](https://mlsanity.com/paper/2302.09844/) (2023)
- [Promise and Peril of Collaborative Code Generation Models: Balancing Effectiveness and Memorization](https://mlsanity.com/paper/2409.12020/) (2024)
- [The Impact of LLM-Assistants on Software Developer Productivity: A Systematic Review and Mapping Study](https://mlsanity.com/paper/2507.03156/) (2025)
- [MLOps Spanning Whole Machine Learning Life Cycle: A Survey](https://mlsanity.com/paper/2304.07296/) (2023)
- [An investigation of licensing of datasets for machine learning based on the GQM model](https://mlsanity.com/paper/2303.13735/) (2023)
- [Decision Models for Selecting Federated Learning Architecture Patterns](https://mlsanity.com/paper/2204.13291/) (2022)
- [A Federated Learning Scheme for Neuro-developmental Disorders: Multi-Aspect ASD Detection](https://mlsanity.com/paper/2211.00643/) (2022)

## Abstract (arXiv)

Federated learning is an emerging machine learning paradigm where clients train models locally and formulate a global model based on the local model updates. To identify the state-of-the-art in federated learning and explore how to develop federated learning systems, we perform a systematic literature review from a software engineering perspective, based on 231 primary studies. Our data synthesis covers the lifecycle of federated learning system development that includes background understanding, requirement analysis, architecture design, implementation, and evaluation. We highlight and summarise the findings from the results, and identify future trends to encourage researchers to advance their current work.