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This paper proposes Fed-Equilibrium, a federated learning framework that balances robustness and fairness in clinical networks using topological Pareto control to ensure minority nodes achieve convergence comparable to dominant hubs.
TopoExplore augments Go-Explore with topological detection of enclosed regions to avoid wasting budget on sealed areas, achieving speedups on MiniGrid and HM3D environments.