mlsanityData to 14 Oct 2026

Personalizing Federated Learning for Hierarchical Edge Networks with Non-IID Data

arXiv 2504.08872 ↗First posted 11 Apr 2025cs.LG cs.AI cs.DCcited by 1

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Abstract (arXiv)

Accommodating edge networks between IoT devices and the cloud server in Hierarchical Federated Learning (HFL) enhances communication efficiency without compromising data privacy. However, devices connected to the same edge often share geographic or contextual similarities, leading to varying edge-level data heterogeneity with different subsets of labels per edge, on top of device-level heterogeneity. This hierarchical non-Independent and Identically Distributed (non-IID) nature, which implies that each edge has its own optimization goal, has been overlooked in HFL research. Therefore, existing edge-accommodated HFL demonstrates inconsistent performance across edges in various hierarchical non-IID scenarios. To ensure robust performance with diverse edge-level non-IID data, we propose a Personalized Hierarchical Edge-enabled Federated Learning (PHE-FL), which personalizes each edge model to perform well on the unique class distributions specific to each edge. We evaluated PHE-FL across 4 scenarios with varying levels of edge-level non-IIDness, with extreme IoT device level non-IIDness. To accurately assess the effectiveness of our personalization approach, we deployed test sets on each edge server instead of the cloud server, and used both balanced and imbalanced test sets. Extensive experiments show that PHE-FL achieves up to 83 percent higher accuracy compared to existing federated learning approaches that incorporate edge networks, given the same number of training rounds. Moreover, PHE-FL exhibits improved stability, as evidenced by reduced accuracy fluctuations relative to the state-of-the-art FedAvg with two-level (edge and cloud) aggregation.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 24 of its 48 references that are in the map. First posted on arXiv 11 Apr 2025.

Builds on

24 of 48 references.

References · 24

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12 more
  1. 2021Towards Fair Federated Learning with Zero-Shot Data Augmentationcited by 64
  2. 2022FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural Networkscited by 23
  3. 2023Leave No Stone Unturned: Mine Extra Knowledge for Imbalanced Facial Expression Recognitioncited by 17
  4. 2021Connecting Low-Loss Subspace for Personalized Federated Learningcited by 12
  5. 2022Personalized Federated Learning via Convex Clusteringcited by 10
  6. 2023Green Federated Learningcited by 8
  7. 2024RBLA: Rank-Based-LoRA-Aggregation for Fine-tuning Heterogeneous Models in FLaaScited by 8
  8. 2023Dynamic Heterogeneous Federated Learning with Multi-Level Prototypescited by 7
  9. 2024Federated Learning as a Service for Hierarchical Edge Networks with Heterogeneous Modelscited by 6
  10. 2022Cluster Based Secure Multi-Party Computation in Federated Learning for Histopathology Imagescited by 4
  11. 2021Federated Multiple Label Hashing (FedMLH): Communication Efficient Federated Learning on Extreme Classification Taskscited by 2
  12. 2024Boosting Communication Efficiency of Federated Learning's Secure Aggregationcited by 2

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Citing papers per year: 2026: 1.

1 paper.

  1. 2026Fed-BAC: Federated Bandit-Guided Additive Clustering in Hierarchical Federated Learningcited by 0