Graph Federated Learning for CIoT Devices in Smart Home Applications

• URL: http://arxiv.org/abs/2212.14395v1
• Date: Thu, 29 Dec 2022 17:57:19 GMT
• Title: Graph Federated Learning for CIoT Devices in Smart Home Applications
• Authors: Arash Rasti-Meymandi, Seyed Mohammad Sheikholeslami, Jamshid Abouei, Konstantinos N. Plataniotis
• Abstract summary: 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. It is capable of yielding up to $2.41%$ higher accuracy than FedAvg in the case of testing the generalization of the models.
• Score: 23.216140264163535
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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