无需标签数据，也能为设备生成个性化模型。
Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions
- 通过前向传播直接生成低维参数模型，无需标签数据训练。
- 理论保证下，未标注客户端也能参与个性化学习并提升性能。
- 适合数据异构性强、标签稀缺的边缘设备场景。
个性化联邦学习在统计异质性数据的设备（客户端）上训练中日益流行。然而，现有方法大多要求客户端具备标注数据以训练或微调其个性化模型。本文提出FLowDUP，一种新方法，仅需对未标注数据进行一次前向传播即可生成个性化模型。模型参数位于低维子空间，实现高效通信与计算。该方法的优化目标基于我们提出的新型归纳多任务PAC-Bayesian泛化界，为未标注客户端提供性能保障。目标结构支持有标签与无标签客户端共同参与训练。实验评估显示，FLowDUP在多种具有不同异质性特征的数据集上表现优异，并通过大量消融实验验证了各组件的有效性。
原文摘要 · Abstract (English)
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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