We introduce FedKDMR, a novel FL paradigm unifying KD constraints with exploration via model recombination. FedKDMR imposes global model consistency and robustness in training through dynamic KD while sufficiently harnessing model recombination-induced perturbations for diverse parameter exploration. We establish convergence guarantees for strongly convex and smooth objectives. Extensive experiments on FL benchmark datasets demonstrate that FedKDMR achieves a superior accuracy-robustness trade-off against state-of-the-art methods when tackling non-independent and identically distributed and heterogeneous data in FL environments

First published: 14 December 2025

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