mlsanityData to 14 Oct 2026

Graph Federated Learning for CIoT Devices in Smart Home Applications

arXiv 2212.14395 ↗First posted 29 Dec 2022cs.LGcited by 4

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Abstract (arXiv)

This paper deals with the problem of statistical and system heterogeneity in a cross-silo Federated Learning (FL) framework where there exist a limited number of Consumer Internet of Things (CIoT) devices in a smart building. We propose a novel Graph Signal Processing (GSP)-inspired aggregation rule based on graph filtering dubbed ``G-Fedfilt''. The proposed aggregator enables a structured flow of information based on the graph's topology. This behavior allows capturing the interconnection of CIoT devices and training domain-specific models. The embedded graph filter is equipped with a tunable parameter which enables a continuous trade-off between domain-agnostic and domain-specific FL. In the case of domain-agnostic, it forces G-Fedfilt to act similar to the conventional Federated Averaging (FedAvg) aggregation rule. The proposed G-Fedfilt also enables an intrinsic smooth clustering based on the graph connectivity without explicitly specified which further boosts the personalization of the models in the framework. In addition, the proposed scheme enjoys a communication-efficient time-scheduling to alleviate the system heterogeneity. This is accomplished by adaptively adjusting the amount of training data samples and sparsity of the models' gradients to reduce communication desynchronization and latency. Simulation results show that the proposed G-Fedfilt achieves up to 3.99% better classification accuracy than the conventional FedAvg when concerning model personalization on the statistically heterogeneous local datasets, while it is capable of yielding up to 2.41% higher accuracy than FedAvg in the case of testing the generalization of the models.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 37 of its 54 references that are in the map. First posted on arXiv 29 Dec 2022.

Builds on

37 of 54 references.

References · 37

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25 more
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Cited by year

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

4 papers.

  1. 2023Privacy-Preserving Graph Machine Learning from Data to Computation: A Surveycited by 10
  2. 2023Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applicationscited by 3
  3. 2025Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practicecited by 3
  4. 2024A Review of Graph-Powered Data Quality Applications for IoT Monitoring Sensor Networkscited by 0