  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. We propose SemiPFL that
supports 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 from
a wide range of application scenarios, from wearable health to IoT, and
demonstrate that SemiPFL outperforms state-of-art federated learning frameworks
under the same assumptions regarding user performance, network footprint, and
computational consumption. We also show that the solution performs well for
users without label or having limited labeled datasets and increasing
performance for increased labeled data and number of users, signifying the
effectiveness of SemiPFL for handling data heterogeneity and limited
annotation. We also demonstrate the stability of SemiPFL for handling user
hardware resource heterogeneity in three real-time scenarios.
