Federated learning has attracted increasing attention to building models
without accessing the raw user data, especially in healthcare. In real
applications, different federations can seldom work together due to possible
reasons such as data heterogeneity and distrust/inexistence of the central
server. In this paper, we propose a novel framework called MetaFed to
facilitate trustworthy FL between different federations. MetaFed obtains a
personalized model for each federation without a central server via the
proposed Cyclic Knowledge Distillation. Specifically, MetaFed treats each
federation as a meta distribution and aggregates knowledge of each federation
in a cyclic manner. The training is split into two parts: common knowledge
accumulation and personalization. Comprehensive experiments on three benchmarks
demonstrate that MetaFed without a server achieves better accuracy compared to
state-of-the-art methods (e.g., 10%+ accuracy improvement compared to the
baseline for PAMAP2) with fewer communication costs.

本文提出了一种名为 MetaFed 的新型框架，通过循环知识蒸馏为每个联邦获得个性化模型，以促进不同联邦间值得信赖的联邦学习，该方法提高了精度并降低了通信成本。