A Novel Federated and Quantum-resistant Security Framework for Edge-cloud Systems

Abstract

As system decentralization heterogeneous resource constraints and the uncertain threat against quantum computing are introduced by rapid growth of edge-cloud computing infrastructures, this develop also raises new security challenges.Traditional security architectures depend on stationary cryptographic deployments and centralized learning approaches which usually result in increased latency, high energy consumption levels, as well as scalability problems.To mitigate the above challenges, this paper presents a Federated Energy-Aware Quantum-Resistant (FEA-QR) framework that combines federated reinforcement learning with an adaptive multi-layer cryptographic orchestration.This approach treats load scheduling and security management as a multi-objective optimization problem, jointly optimizing load distribution efficiency, security entropy, energy consumption and cryptographic latency.The framework dynamically adjusts the level of cryptography based on system status and threat situations to preserve data locality in distributed clusters.We perform an experimental evaluation in a large-scale simulated edgecloud environment with 400,000 workload instances on the top of 25 federated clusters.The results show that the hybrid mechanism achieves an average of 33% lower latency and 29% lower energy than a static and centralized baseline across all experiments, while also retaining high cryptographic entropy and increased attack resistance.These results demonstrate the ability of federated intelligence when coupled with adaptive quantum-resistant security layers to afford scalable, environmentally friendly and reliable protection for future edge-cloud infrastructure.