Automatic Region Discovery for RegionFed

Develop and evaluate a fully implemented automatic region-discovery mechanism for RegionFed that periodically clusters clients using gradient-similarity signals, supports client migration between regions, and warm-starts regional models for newly formed regions.

Background

The evaluated RegionFed system assumes that regional boundaries are predefined. The paper proposes a possible alternative based on computing a client gradient-similarity matrix, applying spectral clustering, periodically revising region assignments, migrating clients between regions, and warm-starting regional models. However, the complete implementation and empirical evaluation of this dynamic discovery procedure are not provided. Resolving this problem would remove the requirement for manually specified regions and test whether gradient-based grouping remains effective as client distributions evolve.

References

We leave full implementation and evaluation to future work.

— RegionFed: Federated Learning for Personalized Query Understanding in Heterogeneous Retail Environments  (2609.05403 - Nguyen et al., 4 Sep 2026) in Appendix, Section 'Supplementary Algorithms and Extensions', subsection 'Future Directions and Extensions', subsubsection 'Automatic Region Discovery'