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Federated Learning for Cellular Networks: Joint User Association and Resource Allocation
Latif U. Khan,Umer Majeed,Choong Seon Hong 한국통신학회 2020 한국통신학회 APNOMS Vol.2020 No.09
Recent years have shown a remarkable interest in federated learning from researchers to make several Internet of Things applications smart. Although, federated learning offers users’ privacy preservation, it has communication resources optimization challenge. In this paper, we consider federated learning for cellular networks. We formulate an optimization problem to jointly minimizes latency and effect of loss in federated learning model accuracy due to channel uncertainties. We decompose the main optimization problem into two sub-problems: resource allocation and device association sub-problems, due to the NP-hard nature of the main optimization problem. To solve these sub-problems, we propose an iterative approach which further uses efficient heuristic algorithms for resource blocks allocation and device association. Finally, we provide numerical results for the validation of our proposed scheme.
Umer Majeed,Latif U. Khan,Choong Seon Hong 한국통신학회 2020 한국통신학회 APNOMS Vol.2020 No.09
Traffic classification (TC) has a principal function in autonomous network management. Recently, deep learning and machine learning-based TC have become popular than the traditional port-based and protocol-based TC due to practices such as port disguise and payload encryption. The flow-based TC is reliable as it relies on time-related statistical features. Federated learning is a distributed machine learning technique to train improvised deep/machine learning models with less privacy distress. The organizations or enterprises having similar business models may take participation in building a federated model for their network traffic characterization. In this study, we build a cross-silo horizontal federated model for TC using flow-based time-related features. The federated model shows comparable performance to the centralized model.