What question did this study set out to answer?

This research aims to enhance personalized federated learning under resource-constrained, heterogeneous conditions.

April 18, 2026Open Access

pFedZKD: A One-Shot Personalized Federated Learning Framework via Evolutionary Architecture Search and Data-Free Distillation

Key Points

This research aims to enhance personalized federated learning under resource-constrained, heterogeneous conditions.
Developed pFedZKD framework for data-free, one-shot federated learning.
Implemented Particle Swarm Optimization-based Neural Architecture Search for client-specific models.
Introduced architecture-agnostic zero-shot knowledge distillation to consolidate client knowledge across diverse models.
Conducted experiments on standard datasets like MNIST and CIFAR-10 to validate effectiveness.
pFedZKD achieved higher personalization accuracy than existing methods.
The framework demonstrated improved global generalization performance.
Significantly reduced communication costs while maintaining privacy.
Successful in handling highly heterogeneous data distributions.

Abstract

Personalized federated learning (PFL) faces significant challenges in resource-constrained edge environments, where strict communication budgets and severe system heterogeneity must be jointly addressed. Although one-shot federated learning reduces communication overhead, existing methods typically impose unified model architectures or rely on coarse manual selection strategies, limiting their adaptability to highly heterogeneous data distributions and restricting personalized representation capability. To overcome these limitations, we propose Personalized Federated Zero-shot Knowledge Distillation (pFedZKD), a data-free one-shot federated learning framework designed for structurally heterogeneous scenarios. The framework follows a decouple-and-reconstruct collaborative paradigm. On the client side (decoupling stage), we introduce Particle Swarm Optimization-based Federated Neural Architecture Search (PSO-FedNAS), a gradient-free neural architecture search method that enables each client to autonomously discover a customized convolutional architecture aligned with its local data distribution, eliminating the need for architectural consistency across clients. On the server side (reconstruction stage), to address parameter-space incompatibility caused by structural heterogeneity, we develop an architecture-agnostic multi-teacher zero-shot knowledge distillation mechanism (Multi-ZSKD). This method synthesizes pseudo-samples in latent space to extract semantic consensus from heterogeneous client models and transfers the aggregated knowledge to a unified global student model without accessing real data. The entire collaborative process is completed within a single communication round, substantially reducing communication cost while enhancing privacy preservation. Extensive experiments on MNIST, FashionMNIST, SVHN, and CIFAR-10 under heterogeneous data settings demonstrate that pFedZKD consistently achieves superior personalization accuracy, global generalization performance, and communication efficiency compared with state-of-the-art PFL methods.

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