Q-Learning-Based Time-Critical Data Aggregation Scheduling in IoT

Recent advancements in Contrastive Local Learning Networks (CLLNs) have demonstrated their potential for reinforcement learning (RL) applications, particularly in energy-limited environments. This study successfully applied Q-learning techniques to simulated CLLNs, showcasing their robustness and low power consumption compared to traditional digital systems.

A recent study has framed the self-healing process of material systems as a Reinforcement Learning (RL) problem within a Markov Decision Process (MDP), demonstrating that RL agents can autonomously derive optimal policies for maintaining structural integrity while managing resource consumption. The research highlighted the superior performance of continuous-action agents, particularly the TD3 agent, in achieving near-complete material recovery compared to traditional heuristic methods.

The introduction of the Non-stationary and Varying-discounting Markov Decision Processes (NVMDP) framework addresses the limitations faced by traditional stationary Markov Decision Processes (MDPs) in non-stationary environments. This framework allows for varying discount rates over time and transitions, making it applicable to both finite and infinite-horizon tasks.

Hi-SAFE, a new framework for Hierarchical Secure Aggregation in Federated Learning (FL), addresses privacy and communication efficiency challenges in resource-constrained environments like IoT and edge networks. It enhances the security of sign-based methods, such as SIGNSGD-MV, by utilizing efficient majority vote polynomials derived from Fermat's Little Theorem.

A new refinement in temporal-difference learning has been proposed, emphasizing first-order Bellman consistency. This approach trains the learned value function to align with both the Bellman targets and their derivatives, enhancing the stability and convergence of reinforcement learning algorithms like Q-learning and actor-critic methods.