mlsanityData to 7 Oct 2026

HyperFed: Hyperbolic Prototypes Exploration with Consistent Aggregation for Non-IID Data in Federated Learning

arXiv 2307.14384 ↗First posted 26 Jul 2023cs.LG cs.AIcited by 8

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Abstract (arXiv)

Federated learning (FL) collaboratively models user data in a decentralized way. However, in the real world, non-identical and independent data distributions (non-IID) among clients hinder the performance of FL due to three issues, i.e., (1) the class statistics shifting, (2) the insufficient hierarchical information utilization, and (3) the inconsistency in aggregating clients. To address the above issues, we propose HyperFed which contains three main modules, i.e., hyperbolic prototype Tammes initialization (HPTI), hyperbolic prototype learning (HPL), and consistent aggregation (CA). Firstly, HPTI in the server constructs uniformly distributed and fixed class prototypes, and shares them with clients to match class statistics, further guiding consistent feature representation for local clients. Secondly, HPL in each client captures the hierarchical information in local data with the supervision of shared class prototypes in the hyperbolic model space. Additionally, CA in the server mitigates the impact of the inconsistent deviations from clients to server. Extensive studies of four datasets prove that HyperFed is effective in enhancing the performance of FL under the non-IID set.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 34 of its 65 references that are in the map. First posted on arXiv 26 Jul 2023.

Builds on

34 of 65 references.

References · 34

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22 more
  1. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  2. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  3. 2021Federated Learning Based on Dynamic Regularizationcited by 555
  4. 2019Federated Learning with Personalization Layerscited by 533
  5. 2021Towards Personalized Federated Learningcited by 462
  6. 2018Hyperbolic Neural Networkscited by 393
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  8. 2017No Fuss Distance Metric Learning using Proxiescited by 225
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  16. 2020Vertically Federated Graph Neural Network for Privacy-Preserving Node Classificationcited by 61
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  18. 2018Large-Margin Classification in Hyperbolic Spacecited by 41
  19. 2021Hyperbolic Busemann Learning with Ideal Prototypescited by 40
  20. 2022Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferringcited by 28
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  22. 2022SphereFed: Hyperspherical Federated Learningcited by 19

Built on by

Cited by year (2026 to 7 Oct)

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

8 papers.

  1. 2023Multimodal Federated Learning with Missing Modality via Prototype Mask and Contrastcited by 17
  2. 2024Rethinking the Representation in Federated Unsupervised Learning with Non-IID Datacited by 12
  3. 2024Estimating before Debiasing: A Bayesian Approach to Detaching Prior Bias in Federated Semi-Supervised Learningcited by 5
  4. 2023Joint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Datacited by 3
  5. 2025FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Modelscited by 2
  6. 2026FlatLand: Personalized Graph Federated Learning via Tailored Lorentz Spacecited by 2
  7. 2025Federated Prototype Graph Learningcited by 1
  8. 2026Multi-Level Analyzation of Imbalance to Resolve Non-IID-Ness in Federated Learningcited by 0