What question did this study set out to answer?

This study aims to address challenges in federated learning, particularly model heterogeneity and communication overhead, through the FedAK framework.

May 25, 2026Scientific Reports0 citationsOpen Access

FedAK: Semi-Supervised One-Shot Framework for Heterogeneous Federated Learning

FedAK: a semi-supervised one-shot framework for heterogeneous federated learning via feature-level attention-based knowledge distillation

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Authors

HSHassan Salman JPJean‐François Pradat‐Peyre SGSonia Guéhis

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Overview

Randomized trial demonstrates improved global learning efficiency in heterogeneous federated learning, suggesting a novel approach to privacy preservation.

Key Points

This study aims to address challenges in federated learning, particularly model heterogeneity and communication overhead, through the FedAK framework.
Developed FedAK, a semi-supervised one-shot FL framework integrating feature-level attention and knowledge distillation.
Clients train local models on private labeled data and send feature representations of a public dataset to the server.
Employed a semi-supervised aggregation strategy using an attention-based module to generate pseudo-labels for a global model.
FedAK consistently outperforms four state-of-the-art one-shot FL methods across four benchmark datasets.
Demonstrated efficiency under heterogeneous and non-IID conditions.
Achieved significant reduction in communication costs by transmitting only feature representations.

Cite This Study

Salman et al. (2026) studied this question.

synapsesocial.com/papers/6a13e8030e02ee3982d32a1a https://doi.org/10.1038/s41598-026-52408-8

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May 25, 2026Scientific Reports0 citationsOpen Access

FedAK: Semi-Supervised One-Shot Framework for Heterogeneous Federated Learning

FedAK: a semi-supervised one-shot framework for heterogeneous federated learning via feature-level attention-based knowledge distillation

View Full Paper

Ask AI

Bookmark

Authors

HSHassan Salman JPJean‐François Pradat‐Peyre SGSonia Guéhis

Discussion

Member takes

Overview

Randomized trial demonstrates improved global learning efficiency in heterogeneous federated learning, suggesting a novel approach to privacy preservation.

Key Points

This study aims to address challenges in federated learning, particularly model heterogeneity and communication overhead, through the FedAK framework.
Developed FedAK, a semi-supervised one-shot FL framework integrating feature-level attention and knowledge distillation.
Clients train local models on private labeled data and send feature representations of a public dataset to the server.
Employed a semi-supervised aggregation strategy using an attention-based module to generate pseudo-labels for a global model.
FedAK consistently outperforms four state-of-the-art one-shot FL methods across four benchmark datasets.
Demonstrated efficiency under heterogeneous and non-IID conditions.
Achieved significant reduction in communication costs by transmitting only feature representations.

Cite This Study

Salman et al. (2026) studied this question.

synapsesocial.com/papers/6a13e8030e02ee3982d32a1a https://doi.org/10.1038/s41598-026-52408-8

View Full Paper

Ask AI

Bookmark