Adaptive sparsity can be integrated into FedLoGe by dynamically adjusting the sparsity ratio in the SSE-C component based on the model's performance during training. By monitoring how different levels of sparsity impact the model's accuracy and convergence rate, the system can automatically optimize the sparsity ratio to achieve better results. This adaptive approach ensures that unnecessary features are pruned while preserving essential information for effective representation learning.