Federated Self-supervised Learning in Computer Vision
Even though edge devices collect massive amounts of image data, it cannot be labeled due to missing interfaces, expertise, or the reluctance of users to label their own private data. SSL is able to address this label deficiency. While SSL has achieved impressive results in settings where data is stored centrally, its performance in federated settings remains relatively unexplored. The present study investigates federated self-supervised learning (FSSL) in the domain of computer vision. First, four FSSL methods based on SimSiam are implemented and evaluated on CIFAR-10.
This work proposes FedAlign, which outperforms FedSimSiam in linear evaluation and achieves highest accuracies in IID settings. Results further show that Hetero-SSL [26] performs best in non-IID settings. Additionally, FSSL is applied to an autonomous driving dataset with the aim of detecting cars. The findings show that the weights obtained through FSSL are able to closely match supervised ImageNet weights, without the need for expensive annotations. The results highlight the potential of FSSL methods in real-
life applications.