Federated Learning Model Aggregation in Heterogenous Aerial and Space Networks

arXiv:2305.16351v2 Announce Type: replace
Abstract: Federated learning offers a promising approach under the constraints of networking and data privacy constraints in aerial and space networks (ASNs), utilizing large-scale private edge data from drones, balloons, and satellites. Existing research has extensively studied the optimization of the learning process, computing efficiency, and communication overhead. An important yet often overlooked aspect is that participants contribute predictive knowledge with varying diversity of knowledge, affecting the quality of the learned federated models. In this paper, …

abstract aerial aggregation arxiv computing constraints cs.ai cs.dc cs.lg data data privacy drones edge edge data efficiency federated learning networking networks optimization privacy process research satellites scale space type