SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge Intelligence

• URL: http://arxiv.org/abs/2203.08176v1
• Date: Tue, 15 Mar 2022 18:09:15 GMT
• Title: SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge Intelligence
• Authors: Arvin Tashakori, Wenwen Zhang, Z. Jane Wang, and Peyman Servati
• Abstract summary: This paper proposes a novel semi-supervised federated learning (SemiPFL) framework to support edge users having no label or limited labeled datasets. In this work, edge users collaborate to train a hyper-network in the server, generating personalized autoencoders for each user. After receiving updates from edge users, the server produces a set of base models for each user, which the users locally aggregate them using their own labeled dataset.
• Score: 15.590672649077817
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in wearable devices and Internet-of-Things (IoT) have led to massive growth in sensor data generated in edge devices. Labeling such massive data for classification tasks has proven to be challenging. In addition, data generated by different users bear various personal attributes and edge heterogeneity, rendering it impractical to develop a global model that adapts well to all users. Concerns over data privacy and communication costs also prohibit centralized data accumulation and training. This paper proposes a novel personalized semi-supervised federated learning (SemiPFL) framework to support edge users having no label or limited labeled datasets and a sizable amount of unlabeled data that is insufficient to train a well-performing model. In this work, edge users collaborate to train a hyper-network in the server, generating personalized autoencoders for each user. After receiving updates from edge users, the server produces a set of base models for each user, which the users locally aggregate them using their own labeled dataset. We comprehensively evaluate our proposed framework on various public datasets and demonstrate that SemiPFL outperforms state-of-art federated learning frameworks under the same assumptions. We also show that the solution performs well for users without labeled datasets or having limited labeled datasets and increasing performance for increased labeled data and number of users, signifying the effectiveness of SemiPFL for handling edge heterogeneity and limited annotation. By leveraging personalized semi-supervised learning, SemiPFL dramatically reduces the need for annotating data and preserving privacy in a wide range of application scenarios, from wearable health to IoT.

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