The field of continual learning and federated learning is rapidly evolving, with a focus on developing innovative methods to address challenges such as catastrophic forgetting, data heterogeneity, and privacy concerns. Recent research has explored the use of dynamic routing, gradient projection, and decentralized cooperation to improve the performance and efficiency of continual learning models. Additionally, there is a growing interest in federated learning, with techniques such as adaptive distillation, personalized models, and zero-shot learning being proposed to enhance the scalability and privacy of federated learning systems. Noteworthy papers in this area include Dynamic Dual-level Defense Routing for Continual Adversarial Training, which proposes a novel framework for continual adversarial training, and Zero-Shot Decentralized Federated Learning, which enables zero-shot adaptation across distributed clients without a central coordinator. These advances have the potential to significantly impact the development of intelligent systems that can learn and adapt in dynamic environments.