A Federated Deep Q Learning (FedDQL) Based Collaborative DependentTask Scheduling and Resource Monitoring in Cloud

Background: Cloud computing is now the most popular way for businesses to run workflow apps. But scheduling tasks and allocating resources efficiently remains hard due to factors such as task dependencies, system heterogeneity, and high computational demands. This paper proposes a scheduling framework, Federated Learning-enabled Deep Q-Learning (FedDQL), to address these problems. The goal is to improve performance, use less energy, and ensure data is handled safely while making the most of available resources. A real-world dataset used for a comparative analysis shows that the proposed method outperforms existing methods. Objective: To develop a secure, energy-efficient, and high-performing task scheduling framework for cloud environments that improves Quality of Service (QoS) through optimized resource allocation and monitoring. Methods: The FedDQL framework is based on the idea of combining Federated Learning (FL) and Deep Q-Learning. People can learn this way without having to give up their data. The Enhanced Multi-Verse Optimization (EMVO) algorithm finds the optimal scheduling settings. For effective resource monitoring, the framework incorporates the Jordan Normal Form-Deep Kronecker Neural Network (JNF-DKNN). Additionally, the Chebyshev Distance-based Fuzzy Self-Defense Algorithm (CD-FSDA) is employed for dynamic virtual machine selection and monitoring. This holistic approach ensures secure, intelligent, and adaptive scheduling within the cloud environment. Results: Experimental evaluation shows that the proposed FedDQL framework outperforms existing approaches, including DQ-HEFT, DDQNEC, DRQL, ANN, and DQL, in terms of execution time, energy consumption, and overall system performance. Discussion: This study presents a DQL-based scheduling framework augmented with federated learning to ensure secure and energy-efficient workflow scheduling in cloud computing. The system enhances resource allocation, minimizes expenses, makespan, and energy consumption, while improving accuracy by incorporating algorithms such as WHA, FedDQLEMVO, JNF-DKNN, and CD-FSDA. Performance assessment using empirical datasets demonstrates exceptional outcomes. Conclusion: This research introduces a FedDQL-based energy-efficient and secure workflow scheduling framework integrating federated learning for privacy protection and multialgorithm optimization (WHA, FedDQL-EMVO, JNF-DKNN, CD-FSDA). The system enhances task scheduling accuracy, reduces energy use, cost, and imbalance, and outperforms existing methods. However, its reliance on simulations, predefined workflows, and simplified energy modeling limits real-world applicability. Future work will emphasize real-world validation, adaptive optimization, and thermal- and SLA-aware scheduling in heterogeneous cloud environments.