# Hierarchical Personalized Federated Learning Over Massive Mobile Edge Computing Networks

arXiv: https://arxiv.org/abs/2303.10580 · HTML: https://mlsanity.com/paper/2303.10580/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 42 references that are in the map.
- First posted on arXiv 19 Mar 2023.

## Builds on (most cited first, 22 of 22)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Personalized Federated Learning with Moreau Envelopes](https://mlsanity.com/paper/2006.08848/) (2020)
- [Exploiting Shared Representations for Personalized Federated Learning](https://mlsanity.com/paper/2102.07078/) (2021)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [Adaptive Personalized Federated Learning](https://mlsanity.com/paper/2003.13461/) (2020)
- [HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients](https://mlsanity.com/paper/2010.01264/) (2020)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Coordinate Descent Algorithms](https://mlsanity.com/paper/1502.04759/) (2015)
- [Lower Bounds and Optimal Algorithms for Personalized Federated Learning](https://mlsanity.com/paper/2010.02372/) (2020)
- [Robust Federated Learning in a Heterogeneous Environment](https://mlsanity.com/paper/1906.06629/) (2019)
- [Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning](https://mlsanity.com/paper/2007.07174/) (2020)
- [Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework](https://mlsanity.com/paper/2002.10671/) (2020)
- [AI based Service Management for 6G Green Communications](https://mlsanity.com/paper/2101.01588/) (2021)
- [Resource-constrained Federated Edge Learning with Heterogeneous Data: Formulation and Analysis](https://mlsanity.com/paper/2110.07567/) (2021)
- [Semi-asynchronous Hierarchical Federated Learning for Cooperative Intelligent Transportation Systems](https://mlsanity.com/paper/2110.09073/) (2021)
- [Semi-Synchronous Personalized Federated Learning over Mobile Edge Networks](https://mlsanity.com/paper/2209.13115/) (2022)
- [Enabling Large-Scale Federated Learning over Wireless Edge Networks](https://mlsanity.com/paper/2109.10489/) (2021)
- [Hierarchical Multi-resource Fair Queueing for Packet Processing](https://mlsanity.com/paper/2208.06091/) (2022)

## Built on by (most cited first, 7 of 7)

- [Adaptive Model Pruning and Personalization for Federated Learning over Wireless Networks](https://mlsanity.com/paper/2309.01816/) (2023)
- [UAV-assisted Unbiased Hierarchical Federated Learning: Performance and Convergence Analysis](https://mlsanity.com/paper/2407.07739/) (2024)
- [PAGE: Equilibrate Personalization and Generalization in Federated Learning](https://mlsanity.com/paper/2310.08961/) (2023)
- [Sequential Federated Learning in Hierarchical Architecture on Non-IID Datasets](https://mlsanity.com/paper/2408.09762/) (2024)
- [Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling](https://mlsanity.com/paper/2506.02422/) (2025)
- [FedPPA: Progressive Parameter Alignment for Personalized Federated Learning](https://mlsanity.com/paper/2510.14698/) (2025)
- [A Unified Framework for Fair and Personalized Decentralized Learning under Communication Constraints](https://mlsanity.com/paper/2608.26493/) (2026)

## 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.