mlsanityData to 7 Oct 2026

Hierarchical Personalized Federated Learning Over Massive Mobile Edge Computing Networks

arXiv 2303.10580 ↗First posted 19 Mar 2023cs.LG cs.AI cs.DCcited by 7

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Abstract (arXiv)

Personalized Federated Learning (PFL) is a new Federated Learning (FL) paradigm, particularly tackling the heterogeneity issues brought by various mobile user equipments (UEs) in mobile edge computing (MEC) networks. However, due to the ever-increasing number of UEs and the complicated administrative work it brings, it is desirable to switch the PFL algorithm from its conventional two-layer framework to a multiple-layer one. In this paper, we propose hierarchical PFL (HPFL), an algorithm for deploying PFL over massive MEC networks. The UEs in HPFL are divided into multiple clusters, and the UEs in each cluster forward their local updates to the edge server (ES) synchronously for edge model aggregation, while the ESs forward their edge models to the cloud server semi-asynchronously for global model aggregation. The above training manner leads to a tradeoff between the training loss in each round and the round latency. HPFL combines the objectives of training loss minimization and round latency minimization while jointly determining the optimal bandwidth allocation as well as the ES scheduling policy in the hierarchical learning framework. Extensive experiments verify that HPFL not only guarantees convergence in hierarchical aggregation frameworks but also has advantages in round training loss maximization and round latency minimization.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 22 of its 42 references that are in the map. First posted on arXiv 19 Mar 2023.

Builds on

22 of 42 references.

References · 22

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10 more
  1. 2020Lower Bounds and Optimal Algorithms for Personalized Federated Learningcited by 122
  2. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
  3. 2019Robust Federated Learning in a Heterogeneous Environmentcited by 88
  4. 2020Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Frameworkcited by 59
  5. 2021AI based Service Management for 6G Green Communicationscited by 14
  6. 2021Resource-constrained Federated Edge Learning with Heterogeneous Data: Formulation and Analysiscited by 10
  7. 2021Semi-asynchronous Hierarchical Federated Learning for Cooperative Intelligent Transportation Systemscited by 6
  8. 2022Semi-Synchronous Personalized Federated Learning over Mobile Edge Networkscited by 5
  9. 2021Enabling Large-Scale Federated Learning over Wireless Edge Networkscited by 3
  10. 2022Hierarchical Multi-resource Fair Queueing for Packet Processingcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 2; 2026: 1.

7 papers.

  1. 2023Adaptive Model Pruning and Personalization for Federated Learning over Wireless Networkscited by 2
  2. 2024UAV-assisted Unbiased Hierarchical Federated Learning: Performance and Convergence Analysiscited by 2
  3. 2023PAGE: Equilibrate Personalization and Generalization in Federated Learningcited by 1
  4. 2024Sequential Federated Learning in Hierarchical Architecture on Non-IID Datasetscited by 1
  5. 2025Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Schedulingcited by 1
  6. 2025FedPPA: Progressive Parameter Alignment for Personalized Federated Learningcited by 0
  7. 2026A Unified Framework for Fair and Personalized Decentralized Learning under Communication Constraintscited by 0