Reinforcement Learning for Key Management in Distributed Systems

The ever-growing paradigms of IoT networks, cloud infrastructures, and blockchain platforms raise demands for adaptive cryptographic key management. Traditional approaches, such as Public Key Infrastructure and Shamir's Secret Sharing, are mostly ineffective in dynamic and scalable scenarios. Reinforcement Learning (RL)-a subfield of AI which possesses adaptation capabilities-can come forward as a promising solution by making the key management system time-efficient against any change or threat in a network. This review covers recent applications of RL in key management, where discussions are focused on Deep RL, Multi-Agent RL, and Federated RL with respect to strengths, use cases, and limitations. We underscore features about scalability, autonomous decision-making, and resilience against key compromise that can be brought forth by RL integration. Finally, we propose a hybrid adaptive framework combining the best aspects from each of these RL approaches for robust key management in dynamic distributed environments.