Hierarchical Federated Learning Across Heterogeneous Cellular Networks
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Abstract (arXiv)
We study collaborative machine learning (ML) across wireless devices, each with its own local dataset. Offloading these datasets to a cloud or an edge server to implement powerful ML solutions is often not feasible due to latency, bandwidth and privacy constraints. Instead, we consider federated edge learning (FEEL), where the devices share local updates on the model parameters rather than their datasets. We consider a heterogeneous cellular network (HCN), where small cell base stations (SBSs) orchestrate FL among the mobile users (MUs) within their cells, and periodically exchange model updates with the macro base station (MBS) for global consensus. We employ gradient sparsification and periodic averaging to increase the communication efficiency of this hierarchical federated learning (FL) framework. We then show using CIFAR-10 dataset that the proposed hierarchical learning solution can significantly reduce the communication latency without sacrificing the model accuracy.
Key facts. Cited by 98 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Sep 2019.
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Citing papers per year: 2021: 25; 2022: 21; 2023: 16; 2024: 18; 2025: 15; 2026: 3.
98 papers.
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- 2023Holonic Learning: A Flexible Agent-based Distributed Machine Learning Framework29 Dec 2023
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- 2023Private Aggregation in Hierarchical Wireless Federated Learning with Partial and Full Collusion25 Jun 2023
- 2023Data-Heterogeneous Hierarchical Federated Learning with Mobility19 Jun 2023
- 2023Asynchronous Multi-Model Dynamic Federated Learning over Wireless Networks: Theory, Modeling, and Optimization22 May 2023
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- 2023Personalized Federated Learning on Long-Tailed Data via Adversarial Feature Augmentation27 Mar 2023
- 2023Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devices1 Mar 2023
50 newest of 98 All 98 (JSON)
- 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
- 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 65
- 2022A Survey on Heterogeneous Federated Learningcited by 42
- 2021Quantized Federated Learning under Transmission Delay and Outage Constraintscited by 37
- 2021Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networkscited by 35
- 2021Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraintscited by 34
- 2021Fusion of Federated Learning and Industrial Internet of Things: A Surveycited by 28
- 2021Time-Correlated Sparsification for Communication-Efficient Federated Learningcited by 25
- 2021Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregationcited by 22
- 2024Advances in APPFL: A Comprehensive and Extensible Federated Learning Frameworkcited by 19
- 2022Semi-Decentralized Federated Learning with Collaborative Relayingcited by 16
- 2022Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networkscited by 15
12 most cited of 98 All 98 (JSON)
- 2026Asynchronous Decentralized Federated Learning over Lossy Wireless Links via Reception- and Age-Aware Aggregation9 Jun 2026
- 2026Fed-BAC: Federated Bandit-Guided Additive Clustering in Hierarchical Federated Learning12 May 2026
- 2026Conflict-Aware Client Selection for Multi-Server Federated Learning2 Feb 2026
- 2025Clustered Federated Learning with Hierarchical Knowledge Distillation11 Dec 2025
- 2025Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practice14 Oct 2025
- 2025Beyond Static Knowledge Messengers: Towards Adaptive, Fair, and Scalable Federated Learning for Medical AI5 Oct 2025
- 2025Hierarchical Federated Learning for Social Network with Mobility18 Sep 2025
- 2025Quantum Federated Learning: A Comprehensive Survey21 Aug 2025
- 2025Optimizing Model Splitting and Device Task Assignment for Deceptive Signal Assisted Private Multi-hop Split Learning9 Jul 2025
- 2025Efficient Federated Learning with Timely Update Dissemination8 Jul 2025
- 2025Topology-Aware Differential Privacy in Hierarchical Federated Learning24 Jun 2025
- 2025FedHQ: Hybrid Runtime Quantization for Federated Learning17 May 2025
12 newest of 98 All 98 (JSON)