FeDepth assigns each robot client to multiple clusters using frozen-encoder descriptors and Jeffreys divergence, improving federated depth estimation over hard-clustering baselines in the HPE scenario while performing comparably in the BMR scenario.
O’Reilly (2008)
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FeDepth: Federated Learning for Depth Estimation under Robot Heterogeneity
FeDepth assigns each robot client to multiple clusters using frozen-encoder descriptors and Jeffreys divergence, improving federated depth estimation over hard-clustering baselines in the HPE scenario while performing comparably in the BMR scenario.