  In response to the challenges posed by non-independent and identically
distributed (non-IID) data and the escalating threat of privacy attacks in
Federated Learning (FL), we introduce HyperFedNet (HFN), a novel architecture
that incorporates hypernetworks to revolutionize parameter aggregation and
transmission in FL. Traditional FL approaches, characterized by the
transmission of extensive parameters, not only incur significant communication
overhead but also present vulnerabilities to privacy breaches through gradient
analysis. HFN addresses these issues by transmitting a concise set of
hypernetwork parameters, thereby reducing communication costs and enhancing
privacy protection. Upon deployment, the HFN algorithm enables the dynamic
generation of parameters for the basic layer of the FL main network, utilizing
local database features quantified by embedding vectors as input. Through
extensive experimentation, HFN demonstrates superior performance in reducing
communication overhead and improving model accuracy compared to conventional FL
methods. By integrating the HFN algorithm into the FL framework, HFN offers a
solution to the challenges of non-IID data and privacy threats.
