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.

翻译：针对联邦学习中非独立同分布数据带来的挑战以及日益严峻的隐私攻击威胁，我们提出HyperFedNet（HFN）——一种融合超网络的新型架构，旨在革新联邦学习中的参数聚合与传输机制。传统联邦学习方法因需传输大量参数，不仅导致显著通信开销，还存在通过梯度分析泄露隐私的风险。HFN通过传输精简的超网络参数集来解决上述问题，既能降低通信成本，又可增强隐私保护。在部署阶段，HFN算法以嵌入向量量化的本地数据库特征为输入，实现联邦主网络基础层参数的动态生成。大量实验表明，相较于传统联邦学习方法，HFN在降低通信开销与提升模型准确率方面均具有优越性能。通过将HFN算法集成至联邦学习框架，该方案为应对非独立同分布数据与隐私威胁提供了有效解决方案。