Paper Title
REDUCING THE NEED FOR LABELED DATA IN DISTRIBUTED DEEP LEARNING USING SELF-SUPERVISED LEARNING

Abstract
The rising demand for large-scale deep learning models has revealed the limitations of supervised learning, particularly its reliance on large amounts of labeled data. To address this, Self-Supervised Learning (SSL) has emerged as a promising alternative that uses unlabeled data to generate meaningful representations, reducing the need for costly labeling. However, training SSL models at scale comes with its own challenges, primarily the need for substantial computational resources. This is where distributed deep learning systems play a critical role, ensuring both scalability and efficiency. In this work, we explore how SSL techniques—such as SimCLR, BYOL, and MoCo—can be integrated with distributed computing frameworks. We focus on key strategies like data parallelism, model parallelism, and edge-cloud hybrid architectures to optimize performance. The paper tackles significant challenges such as communication overhead, data heterogeneity, and fault tolerance, which often arise in distributed SSL systems. We introduce an optimization framework designed to improve training efficiency, scalability, and communication reduction. The framework is tested on largescale datasets, including ImageNet and CIFAR-10, where it shows clear improvements in these critical areas. The results highlight the potential of distributed SSL systems to advance applications in fields related to computer vision, natural language processing (NLP), and speech recognition. Ultimately, this research moves us closer to scalable and resource-efficient SSL solutions, enabling AI systems that can generalize effectively without depending on vast labeled datasets. Keywords - Self-Supervised Learning, Distributed Deep Learning, Sim CLR, Computer Vision, BYOL, MoCo, Scalability, Contrastive Learning, Natural Language Processing, Parallelism, Speech Recognition.