This paper presents FedType, a simple yet pioneering framework designed to fill research gaps in heterogeneous model aggregation within federated learning (FL). FedType introduces small identical proxy models for clients, serving as agents for information exchange, ensuring model security, and achieving efficient communication simultaneously. To transfer knowledge between large private and small proxy models on clients, we propose a novel uncertainty-based asymmetrical reciprocity learning method, eliminating the need for any public data. Comprehensive experiments conducted on benchmark datasets demonstrate the efficacy and generalization ability of FedType across diverse settings. Our approach redefines federated learning paradigms by bridging model heterogeneity, eliminating reliance on public data, prioritizing client privacy, and reducing communication costs.

翻译：本文提出FedType，一个简洁而开创性的框架，旨在填补联邦学习（FL）中异构模型聚合的研究空白。FedType为客户端引入小型相同的代理模型，作为信息交换的媒介，在确保模型安全的同时实现高效通信。为了在客户端的大型私有模型与小型代理模型之间传递知识，我们提出了一种新颖的基于不确定性的非对称互惠学习方法，无需任何公共数据。在基准数据集上进行的全面实验证明了FedType在不同设置下的有效性和泛化能力。我们的方法通过桥接模型异质性、消除对公共数据的依赖、优先保障客户端隐私并降低通信成本，重新定义了联邦学习范式。