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arXiv · 2505.15579

Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions

Hossein Zakerinia·Jonathan Scott·Christoph H. Lampert
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Abstract

Personalized federated learning has emerged as a popular approach to training on devices holding statistically heterogeneous data, known as clients. However, most existing approaches require a client to have labeled data for training or finetuning in order to obtain their own personalized model. In this paper we address this by proposing FLowDUP, a novel method that is able to generate a personalized model using only a forward pass with unlabeled data. The generated model parameters reside in a low-dimensional subspace, enabling efficient communication and computation. FLowDUP's learning objective is theoretically motivated by our new transductive multi-task PAC-Bayesian generalization bound, that provides performance guarantees for unlabeled clients. The objective is structured in such a way that it allows both clients with labeled data and clients with only unlabeled data to contribute to the training process. To supplement our theoretical results we carry out a thorough experimental evaluation of FLowDUP, demonstrating strong empirical performance on a range of datasets with differing sorts of statistically heterogeneous clients. Through numerous ablation studies, we test the efficacy of the individual components of the method.

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Hossein Zakerinia, Jonathan Scott, Christoph H. Lampert. 2025-05-21. Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions. https://arxiv.org/abs/2505.15579

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