Personalized Federated Learning through Global Knowledge Distillation and Local Head Adaptation

Polycarpo Souza Neto; José Mairton Barros da Silva Júnior; Charles Casimiro Cavalcante

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arXiv · 2609.17284

Personalized Federated Learning through Global Knowledge Distillation and Local Head Adaptation

Polycarpo Souza Neto·José Mairton Barros da Silva Júnior·Charles Casimiro Cavalcante

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Abstract

Statistical heterogeneity limits federated learning when a single global classifier cannot represent client-specific label distributions. In this work, we propose Personalized Federated Knowledge Distillation with Head Adaptation (pFedKDH), which aggregates only the shared backbone, keeps persistent client-specific heads, and uses a recalibrated global head as a teacher during local training. Across MNIST, Fashion-MNIST, CIFAR10, and CIFAR100 under class-wise Dirichlet partitions, pFedKDH obtains the best accuracy in most settings, with accuracy gaps up to 37.67\% over the weakest baseline and consistently low standard deviation across repetitions. Component-wise diagnostics and convergence results support the role of persistent heads and distillation-guided local optimization under label-skewed data.

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Polycarpo Souza Neto, José Mairton Barros da Silva Júnior, Charles Casimiro Cavalcante. 2026-09-15. Personalized Federated Learning through Global Knowledge Distillation and Local Head Adaptation. https://arxiv.org/abs/2609.17284

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