Figure 12 From A Federated Deep Reinforcement Learning Based Trust

Figure 12 From A Federated Deep Reinforcement Learning Based Trust Underwater acoustic sensor networks (uasns) have been widely deployed in many areas, such as marine ranching, naval applications, and marine disaster warning systems. the security of uasns, particularly insider threats, is of growing concern. internal attacks carried out via compromised normal nodes are more damaging and stealthy than external attacks, such as signal stealing, data decryption. Index terms—underwater acoustic sensor networks, trust model, deep reinforcement learning, and federated learning. i. introduction u nderwater acoustic sensor networks (uasns) are innovative paradigms widely applied in underwater en vironment monitoring, disaster warning systems, military de fense, and other underwater based scenarios [1]–[3].

A Scenario Of Federated Deep Reinforcement Learning In Navigation Tasks A novel trust model based on federated deep reinforcement learning is proposed for uasns, and experimental results prove that the proposed scheme exhibits satisfactory performance in terms of improving trust prediction accuracy and energy efficiency. underwater acoustic sensor networks (uasns) have been widely deployed in many areas, such as marine ranching, naval applications, and marine. A trust and energy aware double deep reinforcement learning scheduling strategy for federated learning on iot devices. in international conference on service oriented computing (pp. 319–333). springer. rjoub g, bentahar j, abdel wahab o, et al. deep and reinforcement learning for automated task scheduling in large scale cloud computing systems. Second, acquired trust evidence is fed into the corresponding deep reinforcement learning based local trust model to accomplish trust prediction and model training. finally, a federated learning based update method periodically aggregates and updates the parameters of the local models. Federated learning is a distributed machine learning approach that enables a large number of edge end devices to perform on device training for a single machine learning model, without having to share their own raw data. we consider in this paper a federated learning scenario wherein the local training is carried out on iot devices and the global aggregation is done at the level of an edge.

Electronics Free Full Text Federated Deep Reinforcement Learning Second, acquired trust evidence is fed into the corresponding deep reinforcement learning based local trust model to accomplish trust prediction and model training. finally, a federated learning based update method periodically aggregates and updates the parameters of the local models. Federated learning is a distributed machine learning approach that enables a large number of edge end devices to perform on device training for a single machine learning model, without having to share their own raw data. we consider in this paper a federated learning scenario wherein the local training is carried out on iot devices and the global aggregation is done at the level of an edge. Figure 1. an example of federated learning architecture: client server model. step 1: in the process of setting up a client server based learning system, the coordinator creates an initial model and sends it to each participant. those participants who join later can access the latest global model. Deep q learning [12] is an approach that integrates deep neural networks into q learning to address the limitation of q learning in large scale environments. in deep q learning, the deep neural network receives as input one of the online network’s states and outputs the q values q ( s , a ; σ ) of all possible actions, where σ is the weight.

Applied Sciences Free Full Text Lfdc Low Energy Federated Deep Figure 1. an example of federated learning architecture: client server model. step 1: in the process of setting up a client server based learning system, the coordinator creates an initial model and sends it to each participant. those participants who join later can access the latest global model. Deep q learning [12] is an approach that integrates deep neural networks into q learning to address the limitation of q learning in large scale environments. in deep q learning, the deep neural network receives as input one of the online network’s states and outputs the q values q ( s , a ; σ ) of all possible actions, where σ is the weight.

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