An Approach to Stable Gradient-Descent Adaptation of Higher Order Neural Units

Comments on "Convergence Analysis of Adaptive Exponential Functional Link Network"

This article is to comment on the derivation of the weight-update stability of in-parameter-linear nonlinear learning system with the gradient descent learning rule in the above article. Our comments are not to disqualify the commented article's whole contribution; however, the issues should be pointed out to avoid their proliferation.

Effect of Bodybuilding and Fitness Exercise on Physical Fitness Based on Deep Learning

With the rapid development of society and economy, people's living standards are improving day by day, and increasingly attention is paid to physical health, which has set off a fitness upsurge. The purpose of this paper was to analyze the impact of bodybuilding exercise on physical fitness based on deep learning. It provides a reference for fitness enthusiasts to choose scientific and targeted exercise methods, and provides a theoretical basis for the promotion of bodybuilding and fitness. This paper first gives a general introduction to deep learning and adds image segmentation technology to design experiments for bodybuilding and fitness. The experiment was divided into groups A and B, and control group C. In this paper, recurrent neural network and gated recurrent neural network are introduced to compare and analyze the data, and the stability of data processing with different activation functions is compared. The data results show that under the scientific and reasonable arrangement of exercise conditions, bodybuilding and fitness exercises have a corresponding positive effect on the body shape and posture of the subjects. It is more practical to choose a combination of aerobic and anaerobic exercise. In this paper, based on the deep learning algorithm, compared with the recurrent neural network, the gated recurrent neural network is more suitable for processing sequence problems. In the experimental analysis part, this paper compares and analyzes the experimental results of the data under different activation functions, sigmoid function, and tanh function. It is found that the tanh activation function and the gated recurrent neural network are more stable for data processing. The highest AUC value of the traditional recurrent neural network differs by 0.78 from the highest AUC value of the gated recurrent neural network. The data analysis results are in line with the actual situation.

Privacy-enhanced multi-party deep learning

In multi-party deep learning, multiple participants jointly train a deep learning model through a central server to achieve common objectives without sharing their private data. Recently, a significant amount of progress has been made toward the privacy issue of this emerging multi-party deep learning paradigm. In this paper, we mainly focus on two problems in multi-party deep learning. The first problem is that most of the existing works are incapable of defending simultaneously against the attacks of honest-but-curious participants and an honest-but-curious server without a manager trusted by all participants. To tackle this problem, we design a privacy-enhanced multi-party deep learning framework, which integrates differential privacy and homomorphic encryption to prevent potential privacy leakage to other participants and a central server without requiring a manager that all participants trust. The other problem is that existing frameworks consume high total privacy budget when applying differential privacy for preserving privacy, which leads to a high risk of privacy leakage. In order to alleviate this problem, we propose three strategies for dynamically allocating privacy budget at each epoch to further enhance privacy guarantees without compromising the model utility. Moreover, it provides participants with an intuitive handle to strike a balance between the privacy level and the training efficiency by choosing different strategies. Both analytical and experimental evaluations demonstrate the promising performance of our proposed framework.