Clustered Data Sharing for Non-IID Federated Learning over Wireless Networks

• URL: http://arxiv.org/abs/2302.10747v1
• Date: Fri, 17 Feb 2023 07:11:02 GMT
• Title: Clustered Data Sharing for Non-IID Federated Learning over Wireless Networks
• Authors: Gang Hu, Yinglei Teng, Nan Wang, F. Richard Yu
• Abstract summary: Federated Learning (FL) is a distributed machine learning approach to leverage data from the Internet of Things (IoT) Current FL algorithms face the challenges of non-independent and identically distributed (non-IID) data, which causes high communication costs and model accuracy declines. We propose a clustered data sharing framework which spares the partial data from cluster heads to credible associates through device-to-device (D2D) communication.
• Score: 39.80420645943706
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated Learning (FL) is a novel distributed machine learning approach to leverage data from Internet of Things (IoT) devices while maintaining data privacy. However, the current FL algorithms face the challenges of non-independent and identically distributed (non-IID) data, which causes high communication costs and model accuracy declines. To address the statistical imbalances in FL, we propose a clustered data sharing framework which spares the partial data from cluster heads to credible associates through device-to-device (D2D) communication. Moreover, aiming at diluting the data skew on nodes, we formulate the joint clustering and data sharing problem based on the privacy-preserving constrained graph. To tackle the serious coupling of decisions on the graph, we devise a distribution-based adaptive clustering algorithm (DACA) basing on three deductive cluster-forming conditions, which ensures the maximum yield of data sharing. The experiments show that the proposed framework facilitates FL on non-IID datasets with better convergence and model accuracy under a limited communication environment.

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