A Semi-Federated Active Learning Framework for Unlabeled Online Network Data

Yuwen Zhou, Yuhan Hu (), Jing Sun, Rui He and Wenjie Kang
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Yuwen Zhou: College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
Yuhan Hu: Science and Technology on Information Systems Engineering Laboratory, Changsha 410073, China
Jing Sun: College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
Rui He: College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
Wenjie Kang: Hunan Provincial Key Laboratory of Network Investigational Technology, Hunan Police Academy, Changsha 410125, China

Mathematics, 2023, vol. 11, issue 8, 1-13

Abstract: Federated Learning (FL) is a newly emerged federated optimization technique for distributed data in a federated network. The participants in FL that train the model locally are classified into client nodes. The server node assumes the responsibility to aggregate local models from client nodes without data moving. In this regard, FL is an ideal solution to protect data privacy at each node of the network. However, the raw data generated on each node are unlabeled, making it impossible for FL to apply these data directly to train a model. The large volume of data annotating work prevents FL from being widely applied in the real world, especially for online scenarios, where the data are generated continuously. Meanwhile, the data generated on different nodes tend to be differently distributed. It has been proved theoretically and experimentally that non-independent and identically distributed (non-IID) data harm the performance of FL. In this article, we design a semi-federated active learning (semi-FAL) framework to tackle the annotation and non-IID problems jointly. More specifically, the server node can provide (i) a pre-trained model to help each client node annotate the local data uniformly and (ii) an estimation of the global gradient to help correct the local gradient. The evaluation results demonstrate our semi-FAL framework can efficiently handle unlabeled online network data and achieves high accuracy and fast convergence.

Keywords: network data; federated learning; unlabeled data; heterogeneous data (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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