Using the provided abstract and subsection below, craft a highly technical introduction and background for a survey paper titled "Parallel Processing in Deep Learning: Taxonomy and Privacy Implications in Cloud Environments." The introduction should establish the significance of parallelization in deep learning (DL), the importance and the eventual need that surfaced for large-scale model training and deployment within cloud environments and the privacy implications that it introduced. The background section should outline the evolution of parallelization techniques in DL(note that each of the parallelization techniques mentioned are explored in detail in section 4 so provide content that would complement it here), their key applications, and highlight privacy challenges, especially regarding PII(personally identifiable information) data-sensitive cloud-based implementations. The text should be highly technical and academic 

Abstract: The rapid increase in both data volume and model complexity has prompted a shift towards large-scale deep learning systems that rely on parallel and distributed computing environments for efficient model training and deployment. As models and datasets grow beyond the limits of edge devices, the use of parallel and distributed systems becomes indispensable. This paper provides a comprehensive analysis of the landscape of parallelization techniques in deep learning, classifying them into categories such as data parallelism, layer parallelism, task parallelism, model parallelism, pipeline parallelism, subgraph parallelism, and hybrid approaches. We also explore advanced implementations, including tensor parallelism in Megatron-LM, and frameworks such as GSPMD and DAPPLE. The study highlights the strengths, limitations, and applications of these methods, and addresses privacy concerns and computational challenges associated with distributing large models across edge devices and cloud infrastructure. We delve into privacy-preserving strategies such as federated learning and split learning, focusing on implementations like FedSL, which combines these techniques to address non-IID data distributions. This survey aims to equip researchers and practitioners with an in-depth understanding of the current state and future directions of parallelization in deep learning, emphasizing scalable, efficient, and privacy-preserving solutions for large-scale systems in the context of big data and increasingly stringent data protection regulations.

Structure:

Introduction - To be extracted
Background - To be extracted
Section Preview: 4. Parallelization in Deep Learning: Techniques, Applications, and Overheads
4.1 Basic Parallelization Techniques
Neural network training and inference can become computationally intensive, especially when working with large datasets. To overcome this, multiple processors or computers are employed using parallelization techniques. These methods enable the distribution of workload across machines, resulting in faster and more efficient training processes. Parallelization is vital in distributed machine learning, where it helps manage vast data and complex computations. The techniques can be classified by their granularity, ranging from "top-down" approaches focusing on the entire model or computation graph to "bottom-up" techniques that target individual layers or operators. In many cases, techniques like operator fusion combine these approaches to enhance performance.
The major categories of parallelization include data parallelism, layer parallelism, task parallelism, model parallelism, pipeline parallelism, computation graph parallelism, hybrid parallelism, and multi-modal parallelism, as shown in Figure 10. These methods are often combined to maximize resource utilization and efficiency. The choice of parallelization strategy depends on several factors, including model size, network architecture, and hardware resources.