As organizations increasingly seek to build Foundation Models (FMs) using their own proprietary data, many are adopting private and in-house cloud infrastructures (often in addition to public clouds) to address concerns over cost, data privacy, and data sovereignty. However, these isolated private clouds frequently lack interoperability, creating barriers to cross-institutional collaboration, which is vital for training robust Domain-Specific Foundation Models (DSFMs) that rely on large and diverse datasets. Additionally, underutilized resources in private clouds lead to significant global energy inefficiencies. In this paper, we propose the Federated Large Domain Model System (FLDMS), a conceptual framework designed to facilitate collaborative foundation model development across multiple private cloud environments. We review the necessary enabling technologies, including decentralized protocols for data privacy and Large Language Models (LLMs) for automated orchestration, and present a high-level system design demonstrating how these components can be integrated. By enabling secure and efficient cross-organization cooperation, FLDMS provides a blueprint for building DSFMs while addressing the inefficiencies inherent in siloed private cloud systems.

In standard Deep Neural Network (DNN) based classifiers, the general convention is to omit the activation function in the last (output) layer and directly apply the softmax function on the logits to get the probability scores of each class. In this type of architectures, the loss value of the classifier against any output class is directly proportional to the difference between the final probability score and the label value of the associated class. Standard White-box adversarial evasion attacks, whether targeted or untargeted, mainly try to exploit the gradient of the model loss function to craft adversarial samples and fool the model. In this study, we show both mathematically and experimentally that using some widely known activation functions in the output layer of the model with high temperature values has the effect of zeroing out the gradients for both targeted and untargeted attack cases, preventing attackers from exploiting the model's loss function to craft adversarial samples. We've experimentally verified the efficacy of our approach on MNIST (Digit), CIFAR10 datasets. Detailed experiments confirmed that our approach substantially improves robustness against gradient-based targeted and untargeted attack threats. And, we showed that the increased non-linearity at the output layer has some ad-ditional benefits against some other attack methods like Deepfool attack.

The use of Deep learning in Cyber-Physical Systems (CPSs) is gaining popularity due to its ability to bring intelligence to CPS behaviors. However, both CPSs and deep learning have inherent uncertainty. Such uncertainty, if not handled adequately, can lead to unsafe CPS behavior. The first step toward addressing such uncertainty in deep learning is to quantify uncertainty. Hence, we propose a novel method called NIRVANA (uNcertaInty pRediction ValidAtor iN Ai) for prediction validation based on uncertainty metrics. To this end, we first employ prediction-time Dropout-based Neural Networks to quantify uncertainty in deep learning models applied to CPS data. Second, such quantified uncertainty is taken as the input to predict wrong labels using a support vector machine, with the aim of building a highly discriminating prediction validator model with uncertainty values. In addition, we investigated the relationship between uncertainty quantification and prediction performance and conducted experiments to obtain optimal dropout ratios. We conducted all the experiments with four real-world CPS datasets. Results show that uncertainty quantification is negatively correlated to prediction performance of a deep learning model of CPS data. Also, our dropout ratio adjustment approach is effective in reducing uncertainty of correct predictions while increasing uncertainty of wrong predictions.