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Recent advances in wearable devices and Internet-of-Things (IoT) have led tomassive growth in sensor data generated in edge devices. Labeling such massivedata for classification tasks has proven to be challenging. In addition, datagenerated by different users bear various personal attributes and edgeheterogeneity, rendering it impractical to develop a global model that adaptswell to all users. Concerns over data privacy and communication costs alsoprohibit centralized data accumulation and training. We propose SemiPFL thatsupports edge users having no label or limited labeled datasets and a sizableamount 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 updatesfrom edge users, the server produces a set of base models for each user, whichthe users locally aggregate them using their own labeled dataset. Wecomprehensively evaluate our proposed framework on various public datasets froma wide range of application scenarios, from wearable health to IoT, anddemonstrate that SemiPFL outperforms state-of-art federated learning frameworksunder the same assumptions regarding user performance, network footprint, andcomputational consumption. We also show that the solution performs well forusers without label or having limited labeled datasets and increasingperformance for increased labeled data and number of users, signifying theeffectiveness of SemiPFL for handling data heterogeneity and limitedannotation. We also demonstrate the stability of SemiPFL for handling userhardware resource heterogeneity in three real-time scenarios.

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