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Found 4 papers, 0 papers with code
Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networks
Hierarchical federated learning (HFL) enables distributed training of models across multiple devices with the help of several edge servers and a cloud edge server in a privacy-preserving manner.
The Impact of Time Step Frequency on the Realism of Robotic Manipulation Simulation for Objects of Different Scales
This work evaluates the impact of time step frequency and component scale on robotic manipulation simulation accuracy.
Data-Heterogeneous Hierarchical Federated Learning with Mobility
Federated learning enables distributed training of machine learning (ML) models across multiple devices in a privacy-preserving manner.
Hypergraph-based Multi-Robot Task and Motion Planning
We present a multi-robot task and motion planning method that, when applied to the rearrangement of objects by manipulators, results in solution times up to three orders of magnitude faster than existing methods and successfully plans for problems with up to twenty objects, more than three times as many objects as comparable methods.
