Code and Data Repository for An Adaptive Federated Learning Algorithm with Attenuated Memory on Non-IID and Long-tail Data
该仓库提供了AFLAM算法的代码与数据，通过梯度历史衰减机制动态调整客户端权重，解决数据异构问题，提升联邦学习模型性能，适用于金融和医疗等隐私敏感领域。
Addressing the dual challenges of privacy protection and data sharing in sectors such as finance and healthcare, this repository proposes AFLAM (Adaptive Federated Learning Algorithm with Attenuated Memory). By leveraging the decaying mechanism of gradient history to dynamically adjust client weights, this method effectively tackles data heterogeneity and significantly enhances the overall performance of the federated learning model.