>-

A new method called FSL-SAGE is proposed to improve federated split learning by estimating server gradients through auxiliary models, reducing communication costs and client memory needs while maintaining convergence rates comparable to FedAvg. This approach outperforms existing state-of-the-art methods in both efficiency and accuracy, addressing limitations of traditional federated and split learning techniques.