A Knowledge Distillation-empowered Adaptive Federated Reinforcement Learning Framework for Multi-Domain IoT Applications Scheduling
Wang, Zhiyu Goudarzi, Mohammad Gong, Mingming Buyya, Rajkumar
Wang, Zhiyu
Goudarzi, Mohammad
Gong, Mingming
Buyya, Rajkumar
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Department
Machine Learning
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Journal article
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English
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Abstract
The rapid proliferation of Internet of Things (IoT) applications across heterogeneous Cloud-Edge-IoT environments presents significant challenges in distributed scheduling optimization. Existing approaches face issues, including fixed neural network architectures that are incompatible with computational heterogeneity, non-Independent and Identically Distributed (non IID) data distributions across IoT scheduling domains, and insufficient cross-domain collaboration mechanisms. This pa per proposes KD-AFRL, a Knowledge Distillation-empowered Adaptive Federated Reinforcement Learning framework that addresses multi-domain IoT application scheduling through three core innovations. First, we develop a resource-aware hybrid architecture generation mechanism that creates dual-zone neural networks enabling heterogeneous devices to participate in collaborative learning while maintaining optimal resource utilization. Second, we propose a privacy-preserving environment-clustered federated learning approach that utilizes differential privacy and K-means clustering to address non-IID challenges and facilitate effective collaboration among compatible domains. Third, we introduce an environment-oriented cross-architecture knowledge distillation mechanism that enables efficient knowledge transfer between heterogeneous models through temperature-regulated soft targets. Comprehensive experiments with real Cloud-Edge IoT infrastructure demonstrate KD-AFRL's effectiveness using diverse IoT applications. Results show significant improvements over the best baseline, with 21% faster convergence and 15.7%, 10.8%, and 13.9% performance gains in completion time, energy consumption, and weighted cost, respectively. Scalability experiments reveal that KD-AFRL achieves 3-5 times better performance retention compared to existing solutions as the number of domains increases.
Citation
Z. Wang, M. Goudarzi, M. Gong, R. Buyya, "A Knowledge Distillation-empowered Adaptive Federated Reinforcement Learning Framework for Multi-Domain IoT Applications Scheduling," IEEE Transactions on Mobile Computing, vol. PP, no. 99, pp. 1-17, 2026, https://doi.org/10.1109/tmc.2026.3672603.
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IEEE Transactions on Mobile Computing
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46 Information and Computing Sciences, 4602 Artificial Intelligence, 4606 Distributed Computing and Systems Software, 4611 Machine Learning, 7 Affordable and Clean Energy
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IEEE