## 4 Organizational Experimental Setup
### Experimental Setup 2
The plotted graph provides a clear visual representation of this trend, further solidifying the observation of a consistent computational time. [PERSON]'s loss starts at approximately 0.258 and steadily decreases to around 0.023 by step 126,000. [PERSON]'s loss starts at approximately 0.257 and decreases to around 0.027 by step 126,000. [PERSON]'s loss seems to oscillate around the range of 0.022 to 0.025. While it does vary, there is no drastic drop or increase, suggesting the model is stabilizing but not necessarily converging to a very low value. [PERSON]'s loss is similarly in the range of 0.024 to 0.027. [PERSON]'s eavesdropping capability, based on her loss, evolved from an initial undefined state at step 0, reaching a loss of 0.024 by step 500,000. This showcases [PERSON]'s consistent attempts to intercept the encrypted communication, with varying degrees of success. From the onset at step 0 through to step 500,000, [PERSON] and [PERSON]'s encrypted communication system has seen consistent refinements, reflected in the steadiness of their loss metrics. Nevertheless, the narrow gap between [PERSON]'s loss and [PERSON]'s loss signals shows that, while there have been improvements, the encryption mechanism is not wholly resistant to potential eavesdropping. This emphasizes the necessity for continued advancements and  
Figure 8: Reconstruction error of [PERSON] and [PERSON] during training during experimental setup 2.  
system enhancement to ensure optimal confidentiality. It appears that as the model trains, [PERSON] is getting better with each increasing training step, [PERSON]'s loss is generally lower than [PERSON]'s, but the margin is very nominal at certain steps. This is likely undesirable in this adversarial setup, as we usually want [PERSON] and [PERSON] to communicate securely, making it harder for [PERSON] to intercept and understand their communication. In Figure 9, upon analyzing the time series data over incremental training steps, it is evident that as the system trains, the time values stabilize and hover around the 20 s mark after an initial anomaly. This suggests that with increased training steps, the system's performance reaches a consistent plateau in terms of execution time. In Figure 10, upon delving into the time metrics of the initial 500,000 training steps, the data underscore a semi-stable pattern in the computation time. The values mainly fluctuate around a mean duration of roughly 20.35 s for every 2000 steps. Figure 10: A line graph tracking the increasing cumulative time, in seconds, for every 2000 training steps up to 500,000, showcasing the escalating time commitment as training advances during experimental setup 2. Figure 9: Time series analysis for training during experimental setup 2. In essence, this architecture leans on neural networks to organically learn and adapt to the intricacies of encryption, decryption, and eavesdropping, instead of being bound by predetermined cryptographic conventions. The choice of the Mish activation function [33] in the hidden layers allows the model to tap into a richer representational space, enabling the network to discover intricate patterns and relationships within the data. The overarching philosophy is to let the networks learn the art of secure communication organically, rather than prescribing a predefined pathway.