Advancements in Deep Learning and Deep Federated Learning Models
A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).
Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 17765
Special Issue Editors
Interests: automated disease diagnosis; deep learning; machine learning; lightweight models; disease segmentation; federated learning; explainable AI
Special Issues, Collections and Topics in MDPI journals
Interests: intelligent sensors; smart city projects
Special Issues, Collections and Topics in MDPI journals
Interests: metaheuristic techniques; deep learning; artificial intelligence; big data analytics; cyber-physical systems; 5G networks; healthcare big data
Special Issue Information
Dear Colleagues,
With the advancements in multimedia technologies, artificial-intelligence-based imaging applications have gained significant attention from computational researchers. Many researchers have utilized deep learning techniques to obtain potential features of images and utilize these features to build artificial intelligence models. However, deep learning techniques still suffer from issues associated with over-fitting, data leakage, and hyper-parameters tuning. To overcome the problem of over-fitting, many researchers have utilized ensemble and federated (collaborative) learning techniques. However, federated learning suffers from the location privacy of the participants. Therefore, some researchers have utilized homomorphic encryption and blockchain techniques to provide security to the participants of federated learning models. Additionally, some researchers have utilized metaheuristic techniques to optimize the hyper-parameters of the deep learning and federated learning models. However, the selection of hyper-parameters is still an open area of research. Therefore, this Special Issue deals with those techniques that utilize imaging datasets to build artificial intelligence models. Advancements in deep learning and deep federated learning models will also be considered.
Dr. Dilbag Singh
Prof. Dr. Heung-No Lee
Dr. Vijay Kumar
Guest Editors
Manuscript Submission Information
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Keywords
- image processing
- computer vision
- deep learning
- deep federated learning