edge computing federated learning
Federated Learning in Vehicular Edge Computing: A Selective Model
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Abstract:
Vehicular edge computing has become one of the hottest research topics in recent years. Federated learning (FL), which enables each vehicle to learn the model from local data and leverages the model aggregation method to learn the global model jointly, is a promising solution for vehicular edge computing, where resources are limited and data privacy and vehicle mobility must be strictly considered. However, FL has some limitations, including server selection, scheduling, and aggregation function selection problems. In this work, we propose a selective FL model to address these issues.
Introduction:
Vehicular edge computing aims to leverage the computation and storage resources available in vehicles to provide better services to passengers and road users. Vehicular edge computing primarily focuses on data processing and storage, which should be performed in vehicular networks with low latency and high reliability. However, vehicular cloud servers and the Internet are prone to frequent disconnections or congestion, which can reduce the quality of service of the vehicular network. To address these issues, federated learning has been proposed to learn the global model jointly and exchange parameters to reduce communication overhead and improve privacy. However, FL also has some limitations, including server selection, scheduling, and aggregation function selection problems.
Content:
The proposed selective FL model aims to introduce a selective server selection and scheduling mechanism and a selective aggregation function selection mechanism to address these issues. First, to select the optimal server, we consider the following criteria: network performance, computational power, and storage capacity. Then, to schedule the computation tasks, we utilize Reinforcement Learning (RL) algorithms to decide the optimal computation offloading policies, which can optimize the energy efficiency and task completion time of vehicles. Finally, to select the best aggregation function, we consider several criteria, including data heterogeneity, data redundancy, and data imbalance, and introduce an adaptive aggregation function selection algorithm to adapt to these criteria.
Conclusion:
In conclusion, vehicular edge computing with FL is an effective and promising solution for vehicular networks. The proposed selective FL model can address the server selection, scheduling, and aggregation function selection problems to improve the performance of vehicular networks. Our performance evaluation results indicate that the proposed selective FL model outperforms the traditional FL model in terms of task completion time and energy consumption. In future work, we plan to explore more flexible and efficient aggregation function selection methods and more accurate server and task scheduling algorithms to better reflect the mobility and timing constraints in vehicular edge computing.
Illustration of the application of federated learning for edge
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Abstract:
Federated Learning (FL) is widely used in edge networks, which aims to make the training process fully decentralized without transferring data to the central server. By aggregating gradients and parameters in the network, the global model can be obtained jointly with data privacy preservation. This paper discusses the challenges and solutions of federated learning in edge networks, including data heterogeneity, data locality, model aggregation, privacy preservation, system scalability, and communication efficiency.
Introduction:
Edge networks have become an essential part of the modern communication infrastructure. In these networks, data is generated and processed at the edge devices, which can improve the efficiency of data sharing, computation, and storage. However, the processing capacity of edge devices is often limited, and data privacy and network connectivity can also be challenging to maintain. To address these issues, federated learning has been proposed as a promising solution, which can learn models jointly without storing data centrally.
Content:
Federated learning in edge networks faces several challenges, including data heterogeneity, data locality, model aggregation, privacy preservation, system scalability, and communication efficiency. To address these challenges, several solutions have been proposed, including adaptive learning rate algorithms, adaptive model updating policies, secure aggregation mechanisms, differential privacy mechanisms, and distributed computing platforms.
Conclusion:
Edge networks have the potential to improve data processing, sharing, and storage in various applications. Federated learning can optimize the efficiency and privacy of data processing in edge networks by enabling joint learning of models without storing data centrally. However, federated learning in edge networks also faces several challenges, including data heterogeneity, data locality, model aggregation, privacy preservation, system scalability, and communication efficiency. To address these challenges, several solutions have been proposed, but more efforts are still needed to improve the performance of federated learning in edge networks.
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