  Federated learning (FL) collaboratively models user data in a decentralized
way. However, in the real world, non-identical and independent data
distributions (non-IID) among clients hinder the performance of FL due to three
issues, i.e., (1) the class statistics shifting, (2) the insufficient
hierarchical information utilization, and (3) the inconsistency in aggregating
clients. To address the above issues, we propose HyperFed which contains three
main 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 guiding
consistent feature representation for local clients. Secondly, HPL in each
client captures the hierarchical information in local data with the supervision
of shared class prototypes in the hyperbolic model space. Additionally, CA in
the server mitigates the impact of the inconsistent deviations from clients to
server. Extensive studies of four datasets prove that HyperFed is effective in
enhancing the performance of FL under the non-IID set.
