A Systematic Literature Review on Federated Machine Learning: From A Software Engineering Perspective
arXiv:2007.11354 · doi:10.1145/3450288
Abstract
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.
Published on ACM Computing Survey. Latest version available here: https://dl.acm.org/doi/10.1145/3450288
References in corpus (10)
- Federated Learning: Challenges, Methods, and Future Directions
- Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregation
- Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networks
- A Crowdsourcing Framework for On-Device Federated Learning
- HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning
- Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems
- Federated Learning with Bayesian Differential Privacy
- PrivFL: Practical Privacy-preserving Federated Regressions on High-dimensional Data over Mobile Networks
- Secure Architectures Implementing Trusted Coalitions for Blockchained Distributed Learning (TCLearn)
- Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis