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Tang, T., Han, Z., Cai, Z., Yu, S., Zhou, X., Oseni, T., et al. (2024) Personalized Federated Graph Learning on Non-IID Electronic Health Records. IEEE Transactions on Neural Networks and Learning Systems, 35, 11843-11856.
https://doi.org/10.1109/tnnls.2024.3370297

has been cited by the following article:

• TITLE: Federated Graph Learning Based on Graph Distance Computing

  AUTHORS: Wei Gao

  KEYWORDS: Federated Learning, Federated Graph Learning, Graph Distance

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.14 No.11, November 1, 2024

  ABSTRACT: Federated learning is a classic of privacy-preserving learning, which enables collaborative learning without sharing data. Structured data has become the mainstream of current applications, where there is a correlation between samples in the same dataset, which can be represented by a graph. Federated graph learning (FGL) is the integration of structured data into federated learning, assuming that each user has structured graph representation data and uses graph learning models for training. This article proposes an FGL algorithm based on graph distance calculation, which determines the similarity of users in terms of graph distance, then clusters users, and aggregates local models of users in the same cluster. This algorithm can adjust the number of clusters by changing the threshold (the larger the threshold, the fewer clusters and the more users in each cluster, and vice versa), thereby controlling the scope of user collaboration.