The concept of gradient conflicts observed in federated learning can be applied to various machine learning domains beyond FL, such as multi-task learning, transfer learning, and domain adaptation. In multi-task learning settings, identifying conflicting gradients between different tasks can help improve task-specific model optimization while maintaining shared knowledge representation across tasks. Similarly, in transfer learning scenarios where source and target domains exhibit significant distribution mismatches, addressing gradient conflicts can aid in aligning feature representations for better knowledge transfer. Domain adaptation tasks could benefit from mitigating gradient conflicts to enhance model adaptation capabilities when faced with non-IID data distributions across domains.