PapersSelected9月16日 10:34

Personalized Federated Learning through Global Knowledge Distillation and Local Head Adaptation

arXiv cs.LG@Polycarpo Souza Neto et al. (3)

Paper

Why we picked it

This paper presents a method that could improve the performance of federated learning systems, which are important for privacy-preserving machine learning applications.

This research paper introduces pFedKDH, a method for federated learning that addresses statistical heterogeneity by aggregating only the shared backbone and keeping persistent client-specific heads.

Body

It uses a recalibrated global head as a teacher during local training.Experiments on MNIST, Fashion-MNIST, CIFAR10, and CIFAR100 show it achieves the best accuracy in most settings, with accuracy gaps up to 37.

67% over the weakest baseline and consistently low standard deviation across repetitions.

Original · arXiv cs.LG

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.

Industry Coordinates & Source Verification

This report is categorized under AITOP's "Papers" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Key entities and technical topics under continuous tracking include federated learning, knowledge distillation, personalization, MNIST.

Source attribution: curated from arXiv cs.LG (@Polycarpo Souza Neto, José Mairton Barros da Silva Júnior, Charles Casimiro Cavalcante).

#federated learning #knowledge distillation #personalization #MNIST #CIFAR10

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