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Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks

arXiv 2301.06646 ↗First posted 17 Jan 2023cs.LG cs.DC cs.NIcited by 11

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Abstract (arXiv)

Federated Learning (FL) has gained increasing interest in recent years as a distributed on-device learning paradigm. However, multiple challenges remain to be addressed for deploying FL in real-world Internet-of-Things (IoT) networks with hierarchies. Although existing works have proposed various approaches to account data heterogeneity, system heterogeneity, unexpected stragglers and scalibility, none of them provides a systematic solution to address all of the challenges in a hierarchical and unreliable IoT network. In this paper, we propose an asynchronous and hierarchical framework (Async-HFL) for performing FL in a common three-tier IoT network architecture. In response to the largely varied delays, Async-HFL employs asynchronous aggregations at both the gateway and the cloud levels thus avoids long waiting time. To fully unleash the potential of Async-HFL in converging speed under system heterogeneities and stragglers, we design device selection at the gateway level and device-gateway association at the cloud level. Device selection chooses edge devices to trigger local training in real-time while device-gateway association determines the network topology periodically after several cloud epochs, both satisfying bandwidth limitation. We evaluate Async-HFL's convergence speedup using large-scale simulations based on ns-3 and a network topology from NYCMesh. Our results show that Async-HFL converges 1.08-1.31x faster in wall-clock time and saves up to 21.6% total communication cost compared to state-of-the-art asynchronous FL algorithms (with client selection). We further validate Async-HFL on a physical deployment and observe robust convergence under unexpected stragglers.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 28 of its 63 references that are in the map. First posted on arXiv 17 Jan 2023.

Builds on

28 of 63 references.

References · 28

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16 more
  1. 2021Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradientscited by 122
  2. 2021Online Coreset Selection for Rehearsal-based Continual Learningcited by 114
  3. 2019Hierarchical Federated Learning Across Heterogeneous Cellular Networkscited by 98
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Cited by year

Citing papers per year: 2023: 1; 2024: 5; 2025: 5.

11 papers.

  1. 2023Time-sensitive Learning for Heterogeneous Federated Edge Intelligencecited by 8
  2. 2024FedAST: Federated Asynchronous Simultaneous Trainingcited by 3
  3. 2024Flight: A FaaS-Based Framework for Complex and Hierarchical Federated Learningcited by 3
  4. 2025Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practicecited by 3
  5. 2024Ensembler: Protect Collaborative Inference Privacy from Model Inversion Attack via Selective Ensemblecited by 1
  6. 2024Asyn2F: An Asynchronous Federated Learning Framework with Bidirectional Model Aggregationcited by 1
  7. 2025Orthogonal Calibration for Asynchronous Federated Learningcited by 1
  8. 2025Client Selection in Federated Learning with Data Heterogeneity and Network Latenciescited by 1
  9. 2024PPS-QMIX: Periodically Parameter Sharing for Accelerating Convergence of Multi-Agent Reinforcement Learningcited by 0
  10. 2025FedOC: Multi-Server FL with Overlapping Client Relays in Wireless Edge Networkscited by 0
  11. 2025Reviving Stale Updates: Data-Free Knowledge Distillation for Asynchronous Federated Learningcited by 0