Personalized federated learning trains personalized models tailored to meet individual client’s specific data distributions. However, global models often introduce irrelevant information into personalized models, reducing communication efficiency and accuracy. We propose MiniPFL, a bi-component framework that selectively prioritizes valuable clients for personalized learning. The first component, a shallow layer holds information that is similar across clients, making it ideal for managing traditional federated aggregations. The second component, a deep layer, harbors more personalized information and allows identifying beneficial clients through mini-federations. Extensive experiments on CIFAR-10, CIFAR-100, and Tiny-Imagenet datasets using the Resnet18 architecture in Pytorch demonstrated that MiniPFL reduces communication rounds by 30% and increases accuracy by 2.7% compared to state-of-the-art methods.

中文翻译：

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MiniPFL：用于分层个性化联邦学习的迷你联盟

个性化联合学习训练定制的个性化模型，以满足个别客户的特定数据分布。然而，全局模型常常将不相关的信息引入到个性化模型中，降低了通信效率和准确性。我们提出 MiniPFL，这是一个双组件框架，有选择地优先考虑有价值的客户以进行个性化学习。第一个组件是浅层，它保存跨客户端的相似信息，使其成为管理传统联合聚合的理想选择。第二个组成部分是深层，包含更多个性化信息，并允许通过小型联盟识别有益的客户。使用 Pytorch 中的 Resnet18 架构对 CIFAR-10、CIFAR-100 和 Tiny-Imagenet 数据集进行的大量实验表明，与最先进的方法相比，MiniPFL 减少了 30% 的通信轮数，并将准确性提高了 2.7%。