The Impact of Federated Learning on Distributed Remote Sensing Archives

Federated learning strategies are being evaluated to address the challenges of training machine learning models on distributed remote sensing archives due to data volume and sovereignty issues. The study finds that FedProx performs better than FedAvg for deeper CNN architectures under conditions of non-IID data, while BSP approaches achieve centralized accuracy but at a higher communication cost.

Read the full article at arXiv cs.CV (Vision)

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