Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey
Open accessAbstract
The ultra-low latency requirements of 5G/6G applications and privacy constraints call for distributed machine learning systems to be deployed at the edge. With its simple yet effective approach, federated learning (FL) is a natural solution for massive user-owned devices in edge computing with distributed and private training data. FL methods based on FedAvg typically follow a naive star topology, ignoring the heterogeneity and hierarchy of the volatile edge computing architectures and topologies in reality. Several other network topologies exist and can address the limitations and bottlenecks of the star topology. This motivates us to survey network topology-related FL solutions. In this paper, we conduct a comprehensive survey of the existing FL works focusing on network topologies. After a brief overview of FL and edge computing networks, we discuss various edge network topologies as well as their advantages and disadvantages. Lastly, we discuss the remaining challenges and future works for applying FL to topology-specific edge networks.
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Securing federated models against adversarial attacks and providing ethical data handling across multiple administrative divisions add layers of complexity.
“Additionally, securing federated models against adversarial attacks and providing ethical data handling across multiple administrative divisions add layers of complexity [25,26].”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [26]
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“Moreover, the article by Wu et al. [34] provided a comprehensive survey of optimized DFL models and algorithms focusing on network topologies.”
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Federated Learning at the Edge: Enabling AI in 5G/6G Networks and Massive IoT
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Diagnosing Machine Learning Problems in Federated Learning Systems: A Case Study
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- Authors
- Wu, J., Dong, F., Leung, H., Zhu, Z., Zhou, J., & Drew, S.
- Affiliations
- University of Calgary (CA)
- Michigan State University (US)
- Venue
- ACM Computing Surveys
- Published
- 2024-06-22
- Volume
- 56
- Issue
- 10
- Pages
- 1-41
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W4394924926
- Cited by
- 130
- References
- 126
- Citations by year
- 2026: 44 · 2025: 56 · 2024: 21 · 2023: 9
- FWCI
- 17.86
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Computing Surveys (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Keywords
- federated learning, edge computing, network topology, star topology, FedAvg, data heterogeneity, distributed learning, privacy-preserving machine learning
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