## 1 Introduction  
Deep learning models trained on massive amounts of data may contain private and sensitive information that is subject to organizational or governmental regulations [1], resulting in serious privacy concerns related to the potential misuse or leakage of training data. Various attacks may compromise the privacy of deep learning frameworks, such as reconstructing [2] or extracting data points [3], stealing parameters [4], or constructing nearly identical models to the targeted models [5]. Collaborative deep-learning scenarios where multiple parties jointly train models further increase these concerns [6]. The privacy problems associated with collaborative deep learning become particularly pertinent in the setting of Cyber-Physical Systems (CPSs), which include the integration between computational and physical processes. CPS applications frequently deal with sensitive data from physical locations, and the collaborative nature of model training poses difficulties with safe data sharing and communication. Medical devices, for example, which are essential components of many CPS applications in healthcare, are particularly vulnerable to privacy threats. These devices deal with extremely sensitive data, such as personal health information and medical records [7]. In response to privacy concerns, privacy-preserving deep learning concepts have emerged [1]. Thesepractices aim to preserve the privacy of training data, the trained model and its parameters, and the input and output in the prediction phase [1; 8; 9]. They use techniques such as Differential Privacy [1], Federated Learning [10], Homomorphic Encryption [11], Secure Multiparty Computation [12], and Trusted Execution Environments [13]. However, these solutions pose significant challenges, such as the trade-off between privacy and utility, the cost of encryption, communication and computation overheads, and the inability to handle complicated deep learning models and enormous datasets. In this study, we propose a privacy-preserving collaborative deep learning framework that preserves the privacy of the client's training data, the model and its parameters, and the client's data point in the inference phase, maintains the utility of deep learning models, and reduces the training time and communication overhead. The framework combines Instance Reduction Techniques [14] and the Restricted Boltzmann Machine [15]. RBM learns the relevant features and generates encodings of input data that preserve the relevant features. Then, an Instance Reduction Technique is used to select the relevant encoded training instances to send to the central server for model training. Using our framework, clients only need to send the selected encoded instances to the server for model training rather than sending all their unencoded data. This approach ensures the complete preservation of privacy for the unsent data while also providing a certain degree of privacy protection for the subset of data that is sent. Our framework is novel, effective, and practical and can be applied to various deep-learning tasks in a privacy-preserving manner. Furthermore, it is suited to the special challenges of Cyber-Physical Systems (CPSs). It prioritizes training data privacy, fulfills real-time decision-making requirements, and allows for deep learning models' utility in CPS applications. The framework's goals of reducing training time and communication overhead effectively address the challenges posed by the time-sensitive and interconnected nature of CPS environments. Our framework satisfies all privacy-preserving requirements specified and guarantees the privacy qualitative metrics [9] by guaranteeing the privacy of the training datasets by sending a small subset of the user-encoded data rather than the entire raw data. Furthermore, the same technique can be employed to protect the privacy of the input in the prediction phase. Additionally, the trained model and its parameters are protected from the training parties because no model sharing takes place.