  The ubiquity of edge devices has led to a growing amount of unlabeled data
produced at the edge. Deep learning models deployed on edge devices are
required to learn from these unlabeled data to continuously improve accuracy.
Self-supervised representation learning has achieved promising performances
using centralized unlabeled data. However, the increasing awareness of privacy
protection limits centralizing the distributed unlabeled image data on edge
devices. While federated learning has been widely adopted to enable distributed
machine learning with privacy preservation, without a data selection method to
efficiently select streaming data, the traditional federated learning framework
fails to handle these huge amounts of decentralized unlabeled data with limited
storage resources on edge. To address these challenges, we propose a
Self-supervised On-device Federated learning framework with coreset selection,
which we call SOFed, to automatically select a coreset that consists of the
most representative samples into the replay buffer on each device. It preserves
data privacy as each client does not share raw data while learning good visual
representations. Experiments demonstrate the effectiveness and significance of
the proposed method in visual representation learning.
