Synchronization of two neural networks through mutual learning is used to exchange the key over a public channel. In the absence of a weight vector from another party, the key challenge with neural synchronization is how to assess the coordination of two communication parties. There is an issue of delay in the current techniques in the synchronization assessment that has an impact on the security and privacy of the neural synchronization. In this paper, to assess the complete coordination of a cluster of neural networks more efficiently and timely, an important strategy for assessing coordination is presented. To approximately determine the degree of synchronization, the frequency of the two networks having the same output in prior iterations is used. The hash is used to determine if both the networks are completely synchronized exactly when a certain threshold is crossed. The improved technique makes absolute coordination between two communication parties using the weight vectors’ has value. In contrast, with existing approaches, two communicating parties who follow the proposed approach will detect complete synchronization sooner. This reduces the effective geometric likelihood. The proposed method, therefore, increases the safety of the protocol for neural key exchange. This proposed technique has been passed through different parametric tests. Simulations of the process show effectiveness in terms of cited results in the paper.

通过相互学习对两个神经网络进行同步可用于在公共通道上交换密钥。在没有来自另一方的权重向量的情况下，神经同步的主要挑战是如何评估两个通信方的协调。当前技术中的同步评估中存在一个延迟问题，该问题会影响神经同步的安全性和保密性。在本文中，为了更有效，更及时地评估神经网络集群的完全协调性，提出了一种评估协调性的重要策略。为了大致确定同步程度，使用在先前迭代中具有相同输出的两个网络的频率。哈希值用于确定在超过特定阈值时两个网络是否完全完全同步。改进的技术利用权重向量的价值使两个通信方之间实现绝对协调。相反，使用现有方法，遵循建议方法的两个通信方将更快地检测到完全同步。这减少了有效的几何可能性。因此，所提出的方法增加了用于神经密钥交换的协议的安全性。这项提议的技术已通过不同的参数测试。该过程的仿真显示了本文引用结果的有效性。使用现有方法，遵循建议方法的两个通信方将更快地检测到完全同步。这减少了有效的几何可能性。因此，所提出的方法增加了用于神经密钥交换的协议的安全性。这项提议的技术已通过不同的参数测试。该过程的仿真显示了本文引用结果的有效性。使用现有方法，遵循建议方法的两个通信方将更快地检测到完全同步。这减少了有效的几何可能性。因此，所提出的方法增加了用于神经密钥交换的协议的安全性。这项提议的技术已通过不同的参数测试。该过程的仿真显示了本文引用结果的有效性。