While the smoothing technique used in FESS-GDA offers several advantages in improving convergence rates and performance in federated minimax optimization, there are potential limitations and drawbacks that should be considered:
Sensitivity to Hyperparameters: The performance of the smoothing technique in FESS-GDA may be sensitive to the choice of hyperparameters such as the smoothing parameter, learning rates, and regularization coefficients. Suboptimal hyperparameter selection could lead to subpar convergence or performance.
Increased Computational Overhead: The introduction of smoothing terms and auxiliary parameters in the algorithm may increase the computational complexity and memory requirements, especially in scenarios with large-scale datasets or complex models.
Convergence to Suboptimal Solutions: In some cases, the smoothing technique could potentially lead to convergence to suboptimal solutions or hinder the algorithm's ability to escape local minima, especially in highly nonconvex optimization landscapes.
To address these limitations, future research could focus on:
Automated Hyperparameter Tuning: Implementing automated hyperparameter tuning techniques to optimize the selection of parameters in the smoothing technique.
Regularization Strategies: Exploring different regularization strategies to prevent overfitting and improve the generalization of the algorithm.
Advanced Smoothing Techniques: Investigating more advanced smoothing techniques or adaptive strategies that dynamically adjust the smoothing parameters during training.
By addressing these limitations, the smoothing technique in FESS-GDA can be further enhanced to improve its robustness and effectiveness in federated minimax optimization.