Energy-efficient federated learning是一种新型的机器学习方法,它利用联合学习技术来实现设备间的数据共享,从而降低了机器学习模型训练的能耗。这种方法主要针对移动设备等具有有限资源的设备进行优化,通过在设备本地进行训练,减少数据传输和云端计算的开销,从而有效降低了训练过程中的能耗。同时,该方法还采用了一系列优化...
AutoFL加速RL培训通过使设备在同一性能类别共享学习结果,从而最小化设备上RL的时间和能量开销。为了减小时间,Q-Learning适合作为RL训练方法。 可扩展性 为了允许大量的参与者,AutoFL可以为同一性能类别的设备开发一个共享的q表,但以预测精度的小损失为代价。 4.1 AutoFL Reinforcement Learning Design(强化学习设计) 核...
Energy-Efficient Federated Learning in IoT Networks Chapter © 2022 Data availability No additional data were used in the study besides the list of the papers retrieved from public databases as shown in the process description. Notes https://gitlab.com/rachid-el-mokadem/fedmlsysrev. References...
Training a model to classify images in a large image dataset, they found any federated learning setup in France emitted less CO2than any centralized setup in both China and the U.S. And in training the speech recognition model, federated learning was more efficient than centralized training in ...
The real-world implementation of federated learning (FL) for smart device-based applications necessitates energy-efficient convergent models due to resource-constrained devices. In the literature, deep learning (DL)–based models are frequently trained on FL to obtain high accuracy and quick convergence...
A Safe Deep Reinforcement Learning Approach for Energy Efficient Federated Learning in Wireless Communication Networks Progressing towards a new era of Artificial Intelligence (AI) - enabled wireless networks, concerns regarding the environmental impact of AI have been raised both in industry and academia...
To reduce devices' energy consumption, we propose energy-efficient strategies for bandwidth allocation and scheduling. They adapt to devices' channel states and computation capacities so as to reduce their sum energy consumption while warranting learning performance. In contrast with the traditional rate-...
Energy-Efficient Federated Learning for Internet of Things: Leveraging In-Network Processing and Hierarchical ClusteringFederated learning (FL) has emerged as a promising solution for the Internet of Things (IoT), facilitating distributed artificial intelligence while ensuring communication efficiency and data...
To devise an energy-efficient FL scheme tailored to the proposed system, we formulate an optimization problem aimed at minimizing the total energy consumption of all UE during the FL training process. This problem encompasses the challenges of computation and communication energy consumption, correspondin...
1REWAFL: Residual Energy and Wireless AwareParticipant Selection for Eff icient FederatedLearning over Mobile DevicesYixuan Li, Student Member, IEEE, Xiaoqi Qin, Member, IEEE, Jiaxiang Geng, Student Member, IEEE, Rui Chen,Yanzhao Hou, Yanmin Gong, Member, IEEE, Miao Pan, Senior Member, IEEE...