Improving Federated Aggregation with Deep Unfolding Networks
This work addresses the problem of client heterogeneity in federated learning for researchers and practitioners, presenting an incremental improvement over existing methods.
The paper tackles performance degradation in federated learning due to client heterogeneity by introducing a deep unfolding network that learns adaptive, unbiased weights for aggregation, achieving improved accuracy and quality-aware aggregation with demonstrated effectiveness in numerical experiments.
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}.