  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}.
