P2P Machine Learning Serverless Architecture
| dc.contributor.author | TOSHIT G 1NH20CE054; SARTHAK RAI 1NH20CE042; ABHISHEK SAHOO 1NH20CS008; APEKSHA SAWARN 1NH20CE003 | |
| dc.date.accessioned | 2025-05-31T09:02:40Z | |
| dc.date.available | 2025-05-31T09:02:40Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Within the rapidly developing field of deep learning, this work presents a novel architecture for safe and efficient asynchronous training in serverless peer-to-peer (P2P) networks. The serverless paradigm combined with a peer-to-peer architecture offers a fault-tolerant and scalable environment for cooperative deep learning. In order to determine the most effective way to divide and distribute data among serverless nodes, the study delves deeply into the complexity of data management. By utilizing asynchronous communication protocols to process data asynchronously, nodes can take part in the training process without the central server. In addition to improving scalability, this decentralized strategy reduces the possibility of bottlenecks that come with conventional centralized structures. A high priority is given to security, with end-to-end encryption being implemented to guarantee the confidentiality of data transfer and model changes. Distributed logging makes it easier to monitor and debug in real time, enabling problems to be found and fixed as they happen. This all-inclusive architecture not only addresses the particular difficulties presented by P2P networks and serverless computing, but it also offers a flexible solution suitable for a broad spectrum of deep learning applications. | |
| dc.identifier.uri | http://192.168.75.5:4000/handle/123456789/19178 | |
| dc.language.iso | en | |
| dc.publisher | NHCE | |
| dc.title | P2P Machine Learning Serverless Architecture | |
| dc.type | Learning Object |