Learnable Aggregate Federated Learning Initialized Based on Contribution Evaluation

ABSTRACT

Federated learning generally suffers from slow convergence and suboptimal model performance due to client drift caused by data heterogeneity. Although extensive research has been devoted to addressing these issues, adaptive optimization algorithms that combine model training states and are based on client contribution evaluation remain scarce. With the increasing diversity of data distributions and complexity of model architectures, the convergence of federated learning models often requires more training iterations. To verify the application value of adaptive strategies in federated learning scenarios, this study proposes a Learnable Aggregate Federated Learning algorithm with initialization based on contribution evaluation (LAFL‐AI). At the initial stage of each training round on the server, the algorithm first quantifies the contribution of each client to the global model by exploring the correlation between local and global gradients, and adaptively initializes the aggregation weights of each local model for the global aggregation process directly based on the results of this contribution evaluation. Meanwhile, to further improve the generalization performance of the global model, a class‐balanced proxy dataset is constructed in this study to train and obtain the optimal weights for global aggregation. Experimental results show that the adaptive initialization strategy for aggregation weights based on contribution evaluation proposed in this paper can not only accelerate the training efficiency of aggregation weights but also significantly improve the convergence speed of the global model. Compared with other baseline algorithms, LAFL‐AI enables fast convergence of the global model and can achieve equivalent or even better training results with fewer server training rounds.