GAN-guided artificial neural collaborative complex computation for efficient neural synchronization

Achieving neural synchronization, one must be able to evaluate the degree of cooperation across Artificial Neural Networks (ANNs) on various sides, regardless of each network's particular weights. However, traditional approaches suffer from delays in evaluating collaboration, thereby jeopardizing the concealment of neural coordination. Furthermore, there is a paucity of study on employing a trustworthy Pseudo-Random Number Generator (PRNG) to produce a common input and reciprocate training a group of ANNs. This paper introduces the use of a Generative Adversarial Network (GAN) to successfully handle these issues and synchronize a collection of neural networks for session key switch over. This approach enables efficient and effective assessment of the final synchronization state among multiple ANNs. Reciprocal learning is employed to achieve synchronization between two neural networks and distribute the neural key through a single channel. When the ANNs have previously generated identical outputs, coordination is assessed based on this criterion. The proposed method offers several advantages, including: (1) the generation of ANN input sequences using a PRNG based on a GAN. Additionally, a neural feed-forward structure is utilized, incorporating inputs from a non-random "counter" to represent the statefulness of the PRNG. (2) Moreover, a complex ANNs ring or B-tree-guided group is leveraged to facilitate reciprocal neuronal alignment, leading to the creation of the session key via the public network, (3) The suggested methodology takes into account simple, geometry, and majority attacks, (4) The proposed strategy enables two communication partners to detect full synchronization more rapidly compared to previous approaches. The effectiveness of this recommended approach was thoroughly tested, and the results indicate its superiority over similar methods described in the existing literature.