• LOKENDRA GOUR
• AKHILESH A. WAOO

In recent years software development industry is looking forward a new trend of development of distributed software. Distributed software systems play a significant role where processing units or entities are scattered and interconnected over the computer networks. Efficiency of computer system is drastically improved by technological advancement in computer hardware systems. There is a great need of deep learning approach to cope with the problem managing massive resources in distributed system. Machine learning (ML) provides the mechanism to solve the critical problems occurred in distributed computing. Deep learning employs multiple processing elements or agents to each resource of distributed system. Data-parallelism is the basic approach for deep learning. Data security and privacy are the major issues to implement machine learning and deep learning methods. This paper is an extensive survey on challenges and problems of distributed system.

Distributed computing, machine learning, deep learning, data parallelism, data security, privacy

"Challenges of Distributed Computing in the context of Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.192-195, June 2019, Available :http://www.jetir.org/papers/JETIR1907H30.pdf

"Challenges of Distributed Computing in the context of Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp192-195, June 2019, Available at : http://www.jetir.org/papers/JETIR1907H30.pdf