The field of federated learning is moving towards addressing the challenges of statistical heterogeneity, adversarial attacks, and resource constraints. Researchers are exploring novel approaches to enhance the robustness and efficiency of federated learning models, including the use of hierarchical architectures, adaptive aggregation methods, and personalized learning frameworks. These innovations aim to improve the performance and scalability of federated learning in various applications, such as vehicular networks and wireless systems. Notable papers in this area include: Secure Cluster-Based Hierarchical Federated Learning in Vehicular Networks, which proposes a multi-level defense strategy to counter adversarial attacks. Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data, which introduces a simple yet effective personalized federated learning framework to address non-IID data distributions. Bayesian Robust Aggregation for Federated Learning, which presents an adaptive approach for robust aggregation of model updates based on Bayesian inference. Small-Scale-Fading-Aware Resource Allocation in Wireless Federated Learning, which proposes a resource allocation strategy using multi-agent reinforcement learning to address small-scale fading in wireless networks. Balancing Client Participation in Federated Learning Using AoI, which introduces an Age of Information-based client selection policy to balance client participation and improve convergence stability.