mlsanityData to 7 Oct 2026

Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey

arXiv 2302.02573 ↗First posted 6 Feb 2023cs.LG cs.DCcited by 23

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Abstract (arXiv)

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.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). Builds on 70 of its 176 references that are in the map. First posted on arXiv 6 Feb 2023.

Builds on

70 of 176 references.

References · 70

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
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38 more
  1. 2020Federated Learning with Matched Averagingcited by 564
  2. 2017Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descentcited by 496
  3. 2021Towards Personalized Federated Learningcited by 462
  4. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
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  6. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 371
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  12. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 247
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  15. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  16. 2021FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropoutcited by 192
  17. 2021Federated Learning for Smart Healthcare: A Surveycited by 182
  18. 2020Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challengescited by 138
  19. 2019BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learningcited by 115
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Citing papers per year: 2023: 3; 2024: 5; 2025: 10; 2026: 5.

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  1. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  2. 2025Ten Challenging Problems in Federated Foundation Modelscited by 20
  3. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  4. 2023FedLE: Federated Learning Client Selection with Lifespan Extension for Edge IoT Networkscited by 3
  5. 2025Equitable Federated Learning with NCAcited by 3
  6. 2025Towards Heterogeneity-Aware and Energy-Efficient Topology Optimization for Decentralized Federated Learning in Edge Environmentcited by 3
  7. 2025Decentralised Resource Sharing in TinyML: Wireless Bilayer Gossip Parallel SGD for Collaborative Learningcited by 2
  8. 2023Federated Learning Model Aggregation in Heterogenous Aerial and Space Networkscited by 1
  9. 2024Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Reviewcited by 1
  10. 2024A Comprehensive Survey on Joint Resource Allocation Strategies in Federated Edge Learningcited by 1
  11. 2024Exposing the Vulnerability of Decentralized Learning to Membership Inference Attacks Through the Lens of Graph Mixingcited by 1
  12. 2025FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devicescited by 1
11 more
  1. 2025A Hybrid Swarm Intelligence Approach for Optimizing Multimodal Large Language Models Deployment in Edge-Cloud-based Federated Learning Environmentscited by 1
  2. 2024Impact of Network Topology on Byzantine Resilience in Decentralized Federated Learningcited by 0
  3. 2025Hubs and Spokes Learning: Efficient and Scalable Collaborative Machine Learningcited by 0
  4. 2025Bringing Multi-Modal Multi-Task Federated Foundation Models to Education Domain: Prospects and Challengescited by 0
  5. 2025FedOC: Multi-Server FL with Overlapping Client Relays in Wireless Edge Networkscited by 0
  6. 2025SGFusion: Stochastic Geographic Gradient Fusion in Federated Learningcited by 0
  7. 2026Agentic Fog: A Policy-driven Framework for Distributed Intelligence in Fog Computingcited by 0
  8. 2026DeFRiS: Silo-Cooperative IoT Applications Scheduling via Decentralized Federated Reinforcement Learningcited by 0
  9. 2026Fed-BAC: Federated Bandit-Guided Additive Clustering in Hierarchical Federated Learningcited by 0
  10. 2026Quantization in Federated Learning: Methods, Challenges and Future Directionscited by 0
  11. 2026FL-MAESTRO: Multi-Agent LLM Orchestration for Resource-Constrained Federated Learningcited by 0