Numerous research studies in the field of federated learning (FL) have attempted to use personalization to address the heterogeneity among clients, one of FL's most crucial and challenging problems. However, existing works predominantly focus on tailoring models. Yet, due to the heterogeneity of clients, they may each require different choices of hyperparameters, which have not been studied so far. We pinpoint two challenges of personalized federated hyperparameter optimization (pFedHPO): handling the exponentially increased search space and characterizing each client without compromising its data privacy. To overcome them, we propose learning a \textsc{H}yper\textsc{P}arameter \textsc{N}etwork (HPN) fed with client encoding to decide personalized hyperparameters. The client encoding is calculated with a random projection-based procedure to protect each client's privacy. Besides, we design a novel mechanism to debias the low-fidelity function evaluation samples for learning HPN. We conduct extensive experiments on FL tasks from various domains, demonstrating the superiority of HPN.

翻译：在联邦学习（FL）领域，大量研究工作试图通过个性化方案解决客户端异质性这一关键且极具挑战性的问题。然而，现有工作主要聚焦于模型定制化。由于客户端异质性的存在，每个客户端可能需要不同的超参数选择，但这一问题迄今尚未得到研究。我们指出个性化联邦超参数优化（pFedHPO）面临的挑战：处理呈指数级增长的搜索空间，以及在不损害客户端数据隐私的前提下实现客户端特征刻画。为克服这些挑战，我们提出学习一个输入客户端编码的超参数网络（HPN）来决策个性化超参数。客户端编码通过基于随机投影的流程计算以保护各客户端隐私。此外，我们设计了一种新机制来消除低保真度函数评估样本的偏差，用于训练HPN。我们在多个领域的联邦学习任务上开展大量实验，证明了HPN的优越性。