The proposed H2FDP framework can be extended to handle dynamic changes in the network topology and trust model by incorporating adaptive control mechanisms that continuously monitor and adjust the parameters based on real-time network conditions. This adaptation can involve re-optimizing the local model training intervals, the fraction of active devices in each subnet, and the step size of the training process to accommodate changes in the network structure. By implementing a feedback loop that assesses the current network topology and trust model at regular intervals, the H2FDP framework can dynamically adjust its configuration to optimize performance, privacy, and resource utilization. Additionally, the framework can incorporate machine learning algorithms that learn and adapt to changing network dynamics, ensuring robustness and efficiency in federated learning tasks.