One potential drawback of focusing on reducing gradient dissimilarity is that it may lead to increased computational complexity or communication costs. In some cases, achieving lower gradient dissimilarity may require more sophisticated algorithms or additional resources, which could impact the scalability of the approach. Additionally, overly focusing on reducing gradient dissimilarity may neglect other important aspects of model training, such as convergence speed or robustness to different data distributions.