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A large amount of useful information is included in the news video, and how to classify the news video information has become an important research topic in the field of multimedia technology. News videos are enormously informative, and employing manual classification methods is too time-consuming and vulnerable to subjective judgment. Therefore, d...
Citations
... Tis article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of systematic manipulation of the publication and peer-review process. ...
With the gradual advancement of technology in the production of consumer goods, the Internet-of-Things (IoT) systems have experienced rapid development, resulting in a massive amount of data that can be processed using deep neural networks. However, annotating the data and training the models require significant manpower, time, and computational resources. Transfer learning can address this problem. Traditional systems rely on centralized servers for transfer learning. Although several studies have proposed distributed systems for direct edge-to-edge instance-based and feature-based transfer learning, they neglect model-based transfer learning within the same domain. This leads to lower learning efficiency, lower privacy protection, and higher transmission costs. Therefore, our study proposes direct edge-to-edge local-learning-assisted model-based transfer learning for a direct edge-to-edge many-to-many model-based transfer learning scenario. The method can transfer model structures and weights between distributed devices without relying on powerful centralized servers. The effectiveness of the proposed approach is demonstrated by applying it to various scenarios.
