Federated adaptive asynchronous clustering algorithm for Wireless Mesh Networks

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Date
2021-10-14
Authors
Qiao, Cheng
Brown, Kenneth N.
Zhang, Fan
Tian, Zhihong
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Institute of Electrical and Electronics Engineers (IEEE)
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Abstract
It is a challenge to generate an accurate machine learning model in a distributed network due to the increased concern in data privacy and high cost in gathering all raw data. This paper presents an adaptive asynchronous distributed clustering algorithm for agents in wireless network to learn the global models, while the privacy is protected. Moreover, the communication cost and clustering quality can be adaptively balanced. The proposed clustering algorithm does not require the number of clusters to be pre-defined. To improve the accuracy of the global model, we propose a bounding boxes based method to fully utilize the shape information of clusters. In addition, we consider different knowledge levels of agent and different requirements about the global model. In experiments on randomly generated network topologies, we demonstrate that methods which do more extensive clustering in each cycle, and which exchange descriptions of cluster shape and density instead of just centroids and data counts, achieve more consistent clustering, in significantly shorter elapsed time. We also show that the proposed methods can learn the same number of clusters as the ground truth when clusters are well separated from each other.
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Keywords
Distributed algorithm , Asynchronous , Clustering algorithm , Wireless Mesh Network
Citation
Qiao, C., Brown, K. N., Zhang, F. and Tian, Z. (2021) 'Federated adaptive asynchronous clustering algorithm for Wireless Mesh Networks', IEEE Transactions on Knowledge and Data Engineering. doi: 10.1109/TKDE.2021.3119550
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