While the representation alignment mechanism offers benefits in maintaining consistency between local and global models, there are potential drawbacks and limitations that need to be addressed:
Overfitting: One limitation of representation alignment is the risk of overfitting, especially when the alignment loss is given too much weight. To address this, the weight term α in the alignment loss function should be carefully tuned to prevent overfitting.
Local Optima: The representation alignment mechanism may lead to local optima where the local models converge to suboptimal solutions that do not align well with the global model. To mitigate this, exploring different optimization strategies or introducing randomness in the alignment process can help escape local optima.
Computational Overhead: The representation alignment mechanism adds computational overhead during training, as it requires additional inference on the global model for alignment calculations. Optimizing the computation process and potentially parallelizing the alignment calculations can help reduce this overhead.
Data Heterogeneity: Representation alignment may struggle with highly heterogeneous data distributions across clients, leading to challenges in aligning representations effectively. Addressing data heterogeneity through data preprocessing or adaptive alignment strategies can help overcome this limitation.