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REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks
Eyad Gad, Gad Gad, Mostafa M. Fouda, Mohamed I. Ibrahem, Muhammad Ismail, Zubair Md Fadlullah
https://arxiv.org/abs/2507.02021

REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks
With the rise of Software-Defined Networking (SDN) for managing traffic and ensuring seamless operations across interconnected devices, challenges arise when SDN controllers share infrastructure with deep learning (DL) workloads. Resource contention between DL training and SDN operations, especially in latency-sensitive IoT environments, can degrade SDN's responsiveness and compromise network performance. Federated Learning (FL) helps address some of these concerns by decentralizing DL training…