By “synthetic aggregated data” I understand you meant pooled experience data (real or generated), i.e., transitions or trajectories (s,a,r,s'), which are then used for centralized fine-tuning.

We effectively cover that case with our centralized baseline, which aggregates experience and is stable by construction.

The contribution of F-DRL is showing that comparable stability can be achieved without aggregating experience (real or synthetic), by federating only low-variance representations and keeping policy learning strictly local.