FedEC: Enhancing model federated averaging via two-sided method

Federated learning is a key method for addressing data privacy and security in distributed AI training. However, non-IID data among local clients often causes issues like client drift, leading to slow and unstable model convergence. Current studies typically use one-sided methods that optimize either the client or server-side with the conventional aggregation method FedAvg. In contrast, we introduce FedEC, a two-sided strategy that combines distinct methods: an elastic aggregation algorithm for optimizing the global model on the server and contrastive learning techniques on the client side to reduce divergence between local and global models. This complementary approach fosters mutual reinforcement between client and server, allowing FedEC to better tackle non-IID data challenges. Results from experiments conducted on some benchmark datasets with different settings show that FedEC offers more efficient training and outperforms previous one-sided algorithms, underscoring the effectiveness of this two-sided approach in federated learning.