Federated Learning Across Decentralized and Unshared Archives for Remote Sensing Image Classification

arXiv:2311.06141v2 Announce Type: replace
Abstract: Federated learning (FL) enables the collaboration of multiple deep learning models to learn from decentralized data archives (i.e., clients) without accessing data on clients. Although FL offers ample opportunities in knowledge discovery from distributed image archives, it is seldom considered in remote sensing (RS). In this paper, as a first time in RS, we present a comparative study of state-of-the-art FL algorithms for RS image classification problems. To this end, we initially provide a systematic …

archives arxiv classification cs.cv decentralized federated learning image sensing type