Deep Learning Scalability: A Survey of Challenges, Techniques, and Future Trends

Different scalability challenges are becoming more prevalent, this up to date comparative analysis examines the scalability challenges and optimization techniques in deep learning (DL) currently being addressed in research, focusing on model complexity, communication overhead, optimization, and resource efficiency. Due to the nature of the conference paper length was limited but comprehensive. It emphasizes the advancements in Neural Architecture Search (NAS) and Automated Machine Learning (AutoML) for automating model optimization, along with distributed frameworks such as ExtraDeep and CAP. The role of DL in advancing next-generation technologies like 6 G networks is explored, particularly in optimizing Intelligent Reflective Surfaces (IRS). The paper also delves into future directions, including model pruning, knowledge distillation, and integration with digital twins, blockchain, and sustainable AI approaches. Through systematic analysis, it provides a comprehensive view of the strategies for scaling DL across domains like NLP, healthcare, and telecommunications.