mlsanityData to 7 Oct 2026

HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association

arXiv 2301.06447 ↗First posted 16 Jan 2023cs.NI cs.AI cs.DCcited by 17

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Abstract (arXiv)

Federated learning (FL) is a promising paradigm that enables collaboratively learning a shared model across massive clients while keeping the training data locally. However, for many existing FL systems, clients need to frequently exchange model parameters of large data size with the remote cloud server directly via wide-area networks (WAN), leading to significant communication overhead and long transmission time. To mitigate the communication bottleneck, we resort to the hierarchical federated learning paradigm of HiFL, which reaps the benefits of mobile edge computing and combines synchronous client-edge model aggregation and asynchronous edge-cloud model aggregation together to greatly reduce the traffic volumes of WAN transmissions. Specifically, we first analyze the convergence bound of HiFL theoretically and identify the key controllable factors for model performance improvement. We then advocate an enhanced design of HiFlash by innovatively integrating deep reinforcement learning based adaptive staleness control and heterogeneity-aware client-edge association strategy to boost the system efficiency and mitigate the staleness effect without compromising model accuracy. Extensive experiments corroborate the superior performance of HiFlash in model accuracy, communication reduction, and system efficiency.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 34 of its 56 references that are in the map. First posted on arXiv 16 Jan 2023.

Builds on

34 of 56 references.

References · 34

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
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  3. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  4. 2019Advances and Open Problems in Federated Learningcited by 3,021
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  7. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  8. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  9. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  10. 2019Asynchronous Federated Optimizationcited by 264
  11. 2019Model Pruning Enables Efficient Federated Learning on Edge Devicescited by 219
  12. 2018Applied Federated Learning: Improving Google Keyboard Query Suggestionscited by 202
22 more
  1. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  2. 2016QSGD: Communication-Efficient SGD via Gradient Quantization and Encodingcited by 147
  3. 2020FedVision: An Online Visual Object Detection Platform Powered by Federated Learningcited by 121
  4. 2022Federated Learning with Label Distribution Skew via Logits Calibrationcited by 108
  5. 2019Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregationcited by 99
  6. 2020TiFL: A Tier-based Federated Learning Systemcited by 95
  7. 2020HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learningcited by 74
  8. 2020FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tierscited by 64
  9. 2020FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoringcited by 61
  10. 2020FedGroup: Efficient Clustered Federated Learning via Decomposed Data-Driven Measurecited by 45
  11. 2015Staleness-aware Async-SGD for Distributed Deep Learningcited by 43
  12. 2019Wireless Federated Distillation for Distributed Edge Learning with Heterogeneous Datacited by 39
  13. 2020Stochastic Client Selection for Federated Learning with Volatile Clientscited by 38
  14. 2020FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cyclingcited by 26
  15. 2020FLeet: Online Federated Learning via Staleness Awareness and Performance Predictioncited by 21
  16. 2020Accelerating Federated Learning over Reliability-Agnostic Clients in Mobile Edge Computing Systemscited by 17
  17. 2020Hierarchical Federated Learning through LAN-WAN Orchestrationcited by 13
  18. 2021Semi-Decentralized Federated Edge Learning for Fast Convergence on Non-IID Datacited by 12
  19. 2020Toward Multiple Federated Learning Services Resource Sharing in Mobile Edge Networkscited by 11
  20. 2021CSAFL: A Clustered Semi-Asynchronous Federated Learning Frameworkcited by 10
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  22. 2020Accurate and Fast Federated Learning via IID and Communication-Aware Groupingcited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 8; 2025: 4; 2026: 3.

17 papers.

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  1. 2024Asteroid: Resource-Efficient Hybrid Pipeline Parallelism for Collaborative DNN Training on Heterogeneous Edge Devicescited by 13
  2. 2024Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networkscited by 11
  3. 2024Hierarchical Federated ADMMcited by 4
  4. 2024FedD2S: Personalized Data-Free Federated Knowledge Distillationcited by 3
  5. 2024DIMAT: Decentralized Iterative Merging-And-Training for Deep Learning Modelscited by 3
  6. 2023FedDD: Toward Communication-efficient Federated Learning with Differential Parameter Dropoutcited by 2
  7. 2025Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoTcited by 2
  8. 2024Reactive Orchestration for Hierarchical Federated Learning Under a Communication Cost Budgetcited by 1
  9. 2025The Impact Analysis of Delays in Asynchronous Federated Learning with Data Heterogeneity for Edge Intelligencecited by 1
  10. 2025A Generalized Hierarchical Federated Learning Framework with Theoretical Guaranteescited by 1
  11. 2023FedAgg: Adaptive Federated Learning with Aggregated Gradientscited by 0
  12. 2024A Hierarchical Federated Learning Approach for the Internet of Thingscited by 0
5 more
  1. 2024Twin Sorting Dynamic Programming Assisted User Association and Wireless Bandwidth Allocation for Hierarchical Federated Learningcited by 0
  2. 2025HEART: Achieving Timely Multi-Model Training for Vehicle-Edge-Cloud-Integrated Hierarchical Federated Learningcited by 0
  3. 2026Mitigating Heterogeneity-Induced Drift in Hierarchical Sign-Based Federated Learningcited by 0
  4. 2026Hierarchical Multi-Task Federated Learning in VANETscited by 0
  5. 2026Upholding Robustness in Federated Learning: Trends, Emerging Strategies, and Research Opportunitiescited by 0