Figure 11 From A Federated Deep Reinforcement Learning Based Trust

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Figure 11 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 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. 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. In this paper, we address this challenge and propose a trust based deep reinforcement learning approach to select the most adequate clients in terms of resource consumption and training time. on top of the client selection mechanism, we embed a transfer learning approach to handle the scarcity of data in some regions and compensate potential. Then, a trust based deep reinforcement learning model is put forward to make the selection decisions. work to use federated learning (fl) [9]–[11] for decision making. fl is an emerging.

Electronics Free Full Text Node Selection Algorithm For Federated In this paper, we address this challenge and propose a trust based deep reinforcement learning approach to select the most adequate clients in terms of resource consumption and training time. on top of the client selection mechanism, we embed a transfer learning approach to handle the scarcity of data in some regions and compensate potential. Then, a trust based deep reinforcement learning model is put forward to make the selection decisions. work to use federated learning (fl) [9]–[11] for decision making. fl is an emerging. 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. The originality of our solution compared to existing federated learning based solutions comes from (1) applying federated learning specifically to the cold start problem; (2) proposing a trust mechanism to derive trust scores for the potential recommenders, followed by a double deep q learning scheduling approach that relies on the trust and.

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. The originality of our solution compared to existing federated learning based solutions comes from (1) applying federated learning specifically to the cold start problem; (2) proposing a trust mechanism to derive trust scores for the potential recommenders, followed by a double deep q learning scheduling approach that relies on the trust and.

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