mlsanityData to 14 Oct 2026

Federated Fuzzy Neural Network with Evolutionary Rule Learning

arXiv 2210.14393 ↗First posted 26 Oct 2022cs.LG cs.NEcited by 5

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Abstract (arXiv)

Distributed fuzzy neural networks (DFNNs) have attracted increasing attention recently due to their learning abilities in handling data uncertainties in distributed scenarios. However, it is challenging for DFNNs to handle cases in which the local data are non-independent and identically distributed (non-IID). In this paper, we propose a federated fuzzy neural network (FedFNN) with evolutionary rule learning (ERL) to cope with non-IID issues as well as data uncertainties. The FedFNN maintains a global set of rules in a server and a personalized subset of these rules for each local client. ERL is inspired by the theory of biological evolution; it encourages rule variations while activating superior rules and deactivating inferior rules for local clients with non-IID data. Specifically, ERL consists of two stages in an iterative procedure: a rule cooperation stage that updates global rules by aggregating local rules based on their activation statuses and a rule evolution stage that evolves the global rules and updates the activation statuses of the local rules. This procedure improves both the generalization and personalization of the FedFNN for dealing with non-IID issues and data uncertainties. Extensive experiments conducted on a range of datasets demonstrate the superiority of the FedFNN over state-of-the-art methods.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 21 of its 51 references that are in the map. First posted on arXiv 26 Oct 2022.

Builds on

21 of 51 references.

References · 21

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  3. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  4. 2019Towards Federated Learning at Scale: System Designcited by 832
  5. 2021Model-Contrastive Federated Learningcited by 766
  6. 2017Federated Multi-Task Learningcited by 672
  7. 2020Federated Learning with Matched Averagingcited by 564
  8. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 541
  9. 2019Agnostic Federated Learningcited by 456
  10. 2019Federated Machine Learning: Concept and Applicationscited by 412
  11. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 283
  12. 2021No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Datacited by 241
9 more
  1. 2019Model Fusion via Optimal Transportcited by 219
  2. 2021Personalized Federated Learning using Hypernetworkscited by 212
  3. 2020Survey of Personalization Techniques for Federated Learningcited by 145
  4. 2019On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithmscited by 132
  5. 2022Layer-wised Model Aggregation for Personalized Federated Learningcited by 96
  6. 2021Federated Reconstruction: Partially Local Federated Learningcited by 79
  7. 2021Personalized Federated Learning with Gaussian Processescited by 76
  8. 2022Hierarchical fuzzy neural networks with privacy preservation for heterogeneous big datacited by 3
  9. 2022Distributed Semi-supervised Fuzzy Regression with Interpolation Consistency Regularizationcited by 2

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Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 2; 2025: 2.

5 papers.

  1. 2022FedTP: Federated Learning by Transformer Personalizationcited by 36
  2. 2024Harmonizing Generalization and Personalization in Federated Prompt Learningcited by 27
  3. 2024Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Methodcited by 22
  4. 2025Generalizable Federated Learning using Client Adaptive Focal Modulationcited by 0
  5. 2025Federated Learning based on Self-Evolving Gaussian Clusteringcited by 0