Federated Fuzzy k-nearest neighbor for classification and regression

This paper introduces Federated Fuzzy k-Nearest Neighbors algorithms, with one variant designed for classification tasks and another for regression tasks. The federated nature of FedFKNN enables collaborative learning while preserving privacy by keeping sensitive data local to the devices. In our proposal, the central server sends the query to each client. The clients process the query using the k-Nearest Neighbors (k-NN) algorithm locally and return their results to the server. Finally, the server aggregates these results, following the corresponding k-NN algorithm, to produce the final output. Experimental results demonstrate the effectiveness of the federated approach, outperforming the results of a non-collaborative local learning approach.

• Publication year

  2025

• Venue

  IEEE International Conference on Fuzzy Systems

• Publication date

  2025-07-06

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1109/FUZZ62266.2025.11152085
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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