Edge-Cloud Hybrid Task Scheduling Using Federated Reinforcement Learning and Adaptive Swarm Intelligence
By: Khushboo Jain and Ambika Aggarwal
References
- Hassan, N., Gillani, S., Ahmed, E., Yaqoob, I., & Imran, M. (2018). The role of edge computing in internet of things. IEEE Communications Magazine, 56(11), 110–115.
- Jain, K., Gupta, M., & Abraham, A. (2021). A review on privacy and security assessment of cloud computing. Journal of Information Assurance and Security, 16, 161–168.
- Chawla, D., Jain, K., Mehra, P. S., Das, A. K., & Bera, B. (2025). Quantum cryptography as a solution for secure wireless sensor networks: Roadmap, challenges and solutions. Internet of Things, 32, Article 101610.
- Wang, H., et al. (2020). Architectural design alternatives based on cloud/edge/fog computing for connected vehicles. IEEE Communications Surveys & Tutorials, 22(4), 2349–2377.
- Avasthi, S., Jain, K., Prakash, A., & Sanwal, T. (2024, February). A blockchain architecture for secure decentralized system and computing. In Proceedings of the 3rd International Conference on Power Electronics and IoT Applications in Renewable Energy and Its Control (PARC) (pp. 415–420).
- Bu, T., et al. (2024). Task scheduling in the internet of things: Challenges, solutions, and future trends. Cluster Computing, 27(1), 1017–1046.
- Aggarwal, A., Kumar, S., Bhansali, A., Alsekait, D. M., & AbdElminaam, D. S. (2024). A novel optimization approach for energy-efficient multiple workflow scheduling in a cloud environment. Computer Systems Science & Engineering, 48(4).
- Alsuwat, H., & Alsuwat, E. (2025). Energy-aware and efficient cluster head selection and routing in wireless sensor networks using improved artificial bee colony algorithm. Peer-to-Peer Networking and Applications, 18(2), Article 65.
- Aggarwal, A., Kumar, S., Bhatt, A., & Shah, M. A. (2022). Solving user priority in cloud computing using enhanced optimization algorithm in workflow scheduling. Computational Intelligence and Neuroscience, 2022(1), Article 7855532.
- Sagar, A. S., Haider, A., & Kim, H. S. (2025). A hierarchical adaptive federated reinforcement learning for efficient resource allocation and task scheduling in hierarchical IoT network. Computer Communications, 229, Article 107969.
- Ghanavati, S., Abawajy, J., & Izadi, D. (2020). An energy-aware task scheduling model using ant-mating optimization in fog computing environment. IEEE Transactions on Services Computing, 15(4), 2007–2017.
- Su, Z., Wang, Y., Luan, T. H., Zhang, N., Li, F., Chen, T., & Cao, H. (2021). Secure and efficient federated learning for smart grid with edge-cloud collaboration. IEEE Transactions on Industrial Informatics, 18(2), 1333–1344.
- Baghban, H., Rezapour, A., Hsu, C. H., Nuannimnoi, S., & Huang, C. Y. (2022). Edge-AI: IoT request service provisioning in federated edge computing using actor-critic reinforcement learning. IEEE Transactions on Engineering Management, 71, 12519–12528.
- Soula, M., Karanika, A., Kolomvatsos, K., Anagnostopoulos, C., & Stamoulis, G. (2022). Intelligent task allocation at the edge based on machine learning and bio-inspired algorithms. Evolving Systems, 13(2), 221–242.
- Ramezani Shahidani, F., Ghasemi, A., Toroghi Haghighat, A., & Keshavarzi, A. (2023). Task scheduling in edge-fog-cloud architecture: A multi-objective load balancing approach using reinforcement learning algorithm. Computing, 105(6), 1337–1359.
- Kar, B., Yahya, W., Lin, Y. D., & Ali, A. (2023). Offloading using traditional optimization and machine learning in federated cloud-edge-fog systems: A survey. IEEE Communications Surveys & Tutorials, 25(2), 1199–1226.
- Kim, D. Y., Lee, D. E., Kim, J. W., & Lee, H. S. (2023). Collaborative policy learning for dynamic scheduling tasks in cloud-edge-terminal IoT networks using federated reinforcement learning. IEEE Internet of Things Journal, 11(6), 10133–10149.
- Wu, H., Gu, A., & Liang, Y. (2024). Federated reinforcement learning-empowered task offloading for large models in vehicular edge computing. IEEE Transactions on Vehicular Technology.
- Shen, W., Lin, W., Wu, W., Wu, H., & Li, K. (2025). Reinforcement learning-based task scheduling for heterogeneous computing in end-edge-cloud environments. Cluster Computing, 28(3), Article 179.
- Shidik, G. F., et al. (2025). Novel unsupervised cluster reinforcement Q-learning in minimizing energy consumption of federated edge cloud. IEEE Access.
- Lilhore, U. K., Simaiya, S., Sharma, Y. K., Rai, A. K., Padmaja, S. M., Nabilal, K. V, … & Alsufyani, H. (2025). Cloud-edge hybrid deep learning framework for scalable IoT resource optimization. Journal of Cloud Computing, 14(1), 5.
- Del-Pozo-Puñal, E., García-Carballeira, F., & Camarmas-Alonso, D. (2023). A scalable simulator for cloud, fog and edge computing platforms with mobility support. Future Generation Computer Systems, 144, 117130.
- Jain, K., Agarwal, A., Agrawal, S., & Aggarwal, A. (2025). Digital twins in modern healthcare: A comprehensive review of architectures, applications, and challenges. Wiley Interdisciplinary Reviews: Computational Statistics, 17(3), Article e70041.
- Shinde, A., Iyer, S., Jain, K., & Sagi, S. (2022). Smart City and Village: Future Trends. In Artificial Intelligence for Smart Cities and Smart Villages: Advanced Technologies, Development, and Challenges (pp. 29-43). Bentham Science Publishers.
- Chawla, D., Jain, K., Mehra, P. S., Das, A. K., & Bera, B. (2025). Quantum cryptography as a solution for secure Wireless Sensor Networks: Roadmap, challenges and solutions. Internet of Things, 32, 101610.
Language: English
Page range: 198 - 212
Published on: Apr 28, 2026
Published by: Cerebration Science Publishing Co., Limited
In partnership with: Paradigm Publishing Services
Publication frequency: 6 issues per year
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© 2026 Khushboo Jain, Ambika Aggarwal, published by Cerebration Science Publishing Co., Limited
This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License.