Sequential decision-making problems typically require significant human supervision and data.

These authors contributed equally to this work.

Federated learning (FL) is a distributed learning framework that leverages commonalities between distributed client datasets to train a global model.

By pairing historical and future trajectories and applying contrastive learning on the encoded feature space, we enforce same-space consistency constraints.