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

• URL: http://arxiv.org/abs/2505.15579v1
• Date: Wed, 21 May 2025 14:30:59 GMT
• Title: Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions
• Authors: Hossein Zakerinia, Jonathan Scott, Christoph H. Lampert,
• Abstract summary: FLowDUP is able to generate a personalized model using only a forward pass with unlabeled data.<n>New transductive multi-task PAC-Bayesian generalization bound provides performance guarantees for unlabeled clients.
• Score: 16.161876130822396
• License: http://creativecommons.org/licenses/by/4.0/
• 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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