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Client-Edge-Cloud Hierarchical Federated Learning

arXiv 1905.06641 ↗First posted 16 May 2019cs.NI cs.LGcited by 202

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Abstract (arXiv)

Federated Learning is a collaborative machine learning framework to train a deep learning model without accessing clients' private data. Previous works assume one central parameter server either at the cloud or at the edge. The cloud server can access more data but with excessive communication overhead and long latency, while the edge server enjoys more efficient communications with the clients. To combine their advantages, we propose a client-edge-cloud hierarchical Federated Learning system, supported with a HierFAVG algorithm that allows multiple edge servers to perform partial model aggregation. In this way, the model can be trained faster and better communication-computation trade-offs can be achieved. Convergence analysis is provided for HierFAVG and the effects of key parameters are also investigated, which lead to qualitative design guidelines. Empirical experiments verify the analysis and demonstrate the benefits of this hierarchical architecture in different data distribution scenarios. Particularly, it is shown that by introducing the intermediate edge servers, the model training time and the energy consumption of the end devices can be simultaneously reduced compared to cloud-based Federated Learning.

Key facts. Cited by 202 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 May 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 36; 2022: 31; 2023: 43; 2024: 45; 2025: 30; 2026: 17.

202 papers.

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  1. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
  2. 2024Mobile Edge Intelligence for Large Language Models: A Contemporary Surveycited by 125
  3. 2023Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networkscited by 95
  4. 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 65
  5. 2021Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregationscited by 50
  6. 2021Federated Learning for Internet of Things: Applications, Challenges, and Opportunitiescited by 49
  7. 2021Hierarchical Federated Learning with Quantization: Convergence Analysis and System Designcited by 47
  8. 2021Flexible Clustered Federated Learning for Client-Level Data Distribution Shiftcited by 47
  9. 2021Optimality and Stability in Federated Learning: A Game-theoretic Approachcited by 40
  10. 2022Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Datacited by 36
  11. 2021Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networkscited by 35
  12. 2022FedKL: Tackling Data Heterogeneity in Federated Reinforcement Learning by Penalizing KL Divergencecited by 29
38 more
  1. 2021Convergence Analysis and System Design for Federated Learning over Wireless Networkscited by 25
  2. 2023Selective Knowledge Sharing for Privacy-Preserving Federated Distillation without A Good Teachercited by 25
  3. 2023Topology-aware Federated Learning in Edge Computing: A Comprehensive Surveycited by 23
  4. 2023Bayesian Federated Learning: A Surveycited by 22
  5. 2021UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approachcited by 20
  6. 2021Context-Aware Online Client Selection for Hierarchical Federated Learningcited by 20
  7. 2024Personalized Federated Continual Learning via Multi-granularity Promptcited by 20
  8. 2021Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligencecited by 18
  9. 2022Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networkscited by 18
  10. 2022Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networkingcited by 18
  11. 2021Architectural Patterns for the Design of Federated Learning Systemscited by 17
  12. 2023HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Associationcited by 17
  13. 2021A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guaranteecited by 16
  14. 2022Semi-Decentralized Federated Learning with Collaborative Relayingcited by 16
  15. 2023Holistic Network Virtualization and Pervasive Network Intelligence for 6Gcited by 14
  16. 2024Federated Distillation: A Surveycited by 14
  17. 2023Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devicescited by 13
  18. 2024The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscapecited by 13
  19. 2021Semi-Decentralized Federated Edge Learning for Fast Convergence on Non-IID Datacited by 12
  20. 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
  21. 2021Semi-Decentralized Federated Edge Learning with Data and Device Heterogeneitycited by 12
  22. 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
  23. 2023FLGo: A Fully Customizable Federated Learning Platformcited by 12
  24. 2024Advances in Robust Federated Learning: A Survey with Heterogeneity Considerationscited by 12
  25. 2024Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directionscited by 12
  26. 2021Hierarchical Over-the-Air Federated Edge Learningcited by 11
  27. 2023Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networkscited by 11
  28. 2024Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networkscited by 11
  29. 2024Training Machine Learning models at the Edge: A Surveycited by 11
  30. 2021Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networkscited by 10
  31. 2021A Graph Federated Architecture with Privacy Preserving Learningcited by 10
  32. 2021Slashing Communication Traffic in Federated Learning by Transmitting Clustered Model Updatescited by 10
  33. 2021Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Datacited by 10
  34. 2022Communication-Efficient Federated Distillation with Active Data Samplingcited by 10
  35. 2022Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Pointcited by 10
  36. 2022Over-the-Air Federated Edge Learning with Hierarchical Clusteringcited by 10
  37. 2023AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devicescited by 10
  38. 2023Adaptive Federated Pruning in Hierarchical Wireless Networkscited by 10

50 most cited of 202 All 202 (JSON)