The performance of Federated learning (FL) is negatively affected by device differences and statistical characteristics between participating clients. To address this issue, we introduce a deep unfolding network (DUN)-based technique that learns adaptive weights that unbiasedly ameliorate the adverse impacts of heterogeneity. The proposed method demonstrates impressive accuracy and quality-aware aggregation. Furthermore, it evaluated the best-weighted normalization approach to define less computational power on the aggregation method. The numerical experiments in this study demonstrate the effectiveness of this approach and provide insights into the interpretability of the unbiased weights learned. By incorporating unbiased weights into the model, the proposed approach effectively addresses quality-aware aggregation under the heterogeneity of the participating clients and the FL environment. Codes and details are \href{https://github.com/shanikairoshi/Improved_DUN_basedFL_Aggregation}{here}.

翻译：联邦学习(FL)的性能受到参与客户端设备差异与统计特性的负面影响。针对该问题，本文引入基于深度展开网络(DUN)的技术，该技术学习自适应权重以无偏地减轻异质性带来的不利影响。所提方法展现出卓越的准确性与质量感知聚合能力。此外，该方法评估了最优权重归一化方案以降低聚合方法的计算开销。数值实验验证了该方法的有效性，并为学习得到的无偏权重可解释性提供了深入见解。通过将无偏权重融入模型，本方法有效解决了参与客户端与FL环境异质性下的质量感知聚合问题。代码及细节详见\href{https://github.com/shanikairoshi/Improved_DUN_basedFL_Aggregation}{此处}。