首页|Research from King Saud University Yields New Findings on Boltzmann Machines (Privacy-Preserving Deep Learning Framework Based on Restricted Boltzmann Machines and Instance Reduction Algorithms)
Research from King Saud University Yields New Findings on Boltzmann Machines (Privacy-Preserving Deep Learning Framework Based on Restricted Boltzmann Machines and Instance Reduction Algorithms)
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A new study on Boltzmann machines is now available. According to news originating from Riyadh, Saudi Arabia, by NewsRx correspondents, research stated, “The combination of collaborative deep learning and Cyber-Physical Systems (CPSs) has the potential to improve decision-making, adaptability, and efficiency in dynamic and distributed environments. However, it brings privacy, communication, and resource restrictions concerns that must be properly addressed for successful implementation in real-world CPS systems.” Funders for this research include King Saud University, Riyadh, Saudi Arabia. Our news editors obtained a quote from the research from King Saud University: “Various privacypreserving techniques have been proposed, but they often add complexity and decrease accuracy and utility. In this paper, we propose a privacy-preserving deep learning framework that combines Instance Reduction Techniques (IR) and the Restricted Boltzmann Machine (RBM) to preserve privacy while overcoming the limitations of other frameworks. The RBM encodes training data to retain relevant features, and IR selects the relevant encoded instances to send to the server for training. Privacy is preserved because only a small subset of the training data is sent to the server. Moreover, it is sent after encoding it using RBM. Experiments show that our framework preserves privacy with little loss of accuracy and a substantial reduction in training time.”
King Saud UniversityRiyadhSaudi ArabiaAsiaAlgorithmsBoltzmann MachineEmerging TechnologiesMachine Learning
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