  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.
