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Title	Deeprb: Deep Resource Broker Based On Clustered Federated Learning For Edge Video Analytics
ID_Doc	18142
Authors	Zhang X.; Debroy S.; Wang P.; Li K.
Year	2025
Published	IEEE Transactions on Network and Service Management
DOI	http://dx.doi.org/10.1109/TNSM.2025.3560657
Abstract	Edge computing plays a crucial role in large-scale and real-time video analytics for smart cities, particularly in environments with massive machine-type communications (mMTC) among IoT devices. Due to the dynamic nature of mMTC, one of the main challenges is to achieve energy-efficient resource allocation and service placement in resource-constrained edge computing environments. In this paper, we introduce DeepRB, a deep learning-based resource broker framework designed for real-time video analytics in edge-native environments. DeepRB develops a two-stage algorithm to address both resource allocation and service placement efficiently. First, it uses a Residual Multilayer Perceptron ( ResMLP) network to approximate traditional iterative resource allocation policies for IoT devices that frequently transition between active and idle states. Second, for service placement, DeepRB leverages a multi-agent federated deep reinforcement learning (DRL) approach that incorporates clustering and knowledge-aware model aggregation. Through extensive simulations, we demonstrate the effectiveness of DeepRB in improving schedulability and scalability compared to baseline edge resource management algorithms. Our results highlight the potential of DeepRB for optimizing resource allocation and service placement for real-time video analytics in dynamic and resource-constrained edge computing environments. © 2004-2012 IEEE.
Author Keywords	edge computing; Energy efficiency; real-time video analytics; resource management; service placement

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