The field of federated learning and digital twins is rapidly advancing, with a focus on addressing challenges related to data privacy, efficiency, and adaptability. Recent developments have explored innovative approaches to fine-tuning foundation models, mitigating catastrophic forgetting, and enabling continual test-time adaptation. Another area of interest is the application of federated learning in low-resource settings, such as healthcare in underserved regions. Additionally, researchers are investigating the use of digital twins in edge computing, with a focus on optimizing quality of experience and distributionally robust optimization. Noteworthy papers include FlexFed, which proposes a novel approach to mitigating catastrophic forgetting in heterogeneous federated learning environments. FedCTTA is another significant contribution, introducing a collaborative approach to continual test-time adaptation in federated learning. ATR-Bench provides a unified framework for analyzing federated learning through three foundational dimensions: adaptation, trust, and reasoning. Overall, these advances are pushing the boundaries of what is possible in federated learning and digital twins, with promising applications in various fields.