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Federated learning (FL) collaboratively models user data in a decentralizedway. However, in the real world, non-identical and independent datadistributions (non-IID) among clients hinder the performance of FL due to threeissues, i.e., (1) the class statistics shifting, (2) the insufficienthierarchical information utilization, and (3) the inconsistency in aggregatingclients. To address the above issues, we propose HyperFed which contains threemain modules, i.e., hyperbolic prototype Tammes initialization (HPTI),hyperbolic prototype learning (HPL), and consistent aggregation (CA). Firstly,HPTI in the server constructs uniformly distributed and fixed class prototypes,and shares them with clients to match class statistics, further guidingconsistent feature representation for local clients. Secondly, HPL in eachclient captures the hierarchical information in local data with the supervisionof shared class prototypes in the hyperbolic model space. Additionally, CA inthe server mitigates the impact of the inconsistent deviations from clients toserver. Extensive studies of four datasets prove that HyperFed is effective inenhancing the performance of FL under the non-IID set.

HyperFed: Hyperbolic Prototypes Exploration with Consistent Aggregation for Non-IID Data in Federated Learning