View source for 17770049. COOPERATIVE TRAINING MIGRATION simplified abstract (RAKUTEN MOBILE, INC.)

=COOPERATIVE TRAINING MIGRATION=


==Organization Name==

[[:Category:RAKUTEN MOBILE, INC.|RAKUTEN MOBILE, INC.]]


[[Category:RAKUTEN MOBILE, INC.]]

==Inventor(s)==

[[:Category:Rehmat Ullah of Belfast (GB)|Rehmat Ullah of Belfast (GB)]][[Category:Rehmat Ullah of Belfast (GB)]]

[[:Category:Di Wu of Belfast (GB)|Di Wu of Belfast (GB)]][[Category:Di Wu of Belfast (GB)]]

[[:Category:Paul Harvey of Tokyo (JP)|Paul Harvey of Tokyo (JP)]][[Category:Paul Harvey of Tokyo (JP)]]

[[:Category:Peter Kilpatrick of Belfast (GB)|Peter Kilpatrick of Belfast (GB)]][[Category:Peter Kilpatrick of Belfast (GB)]]

[[:Category:Ivor Spence of Belfast (GB)|Ivor Spence of Belfast (GB)]][[Category:Ivor Spence of Belfast (GB)]]

[[:Category:Blesson Varghese of Belfast (GB)|Blesson Varghese of Belfast (GB)]][[Category:Blesson Varghese of Belfast (GB)]]

==COOPERATIVE TRAINING MIGRATION - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17770049 titled 'COOPERATIVE TRAINING MIGRATION

==Simplified Explanation==
The patent application describes a method for cooperatively training a neural network model using a computational device connected through a network. During training iterations, a data checkpoint is created containing gradient values, weight values, loss value, and optimizer state of the server partition. A migration notice is received during training iterations, indicating the transfer of the data checkpoint to a second edge server.

* Training a neural network model cooperatively with a computational device through a network
* Creating a data checkpoint during training iterations containing gradient values, weight values, loss value, and optimizer state
* Receiving a migration notice during training iterations for transferring the data checkpoint to a second edge server

==Key Features and Innovation==
* Cooperative training migration of neural network models
* Creation of data checkpoints during training iterations
* Transfer of data checkpoints to a second edge server based on migration notices

==Potential Applications==
* Edge computing
* Distributed machine learning systems
* Network optimization

==Problems Solved==
* Efficient training of neural network models across multiple edge servers
* Seamless migration of training data checkpoints
* Improved network performance during cooperative training

==Benefits==
* Faster training of neural network models
* Enhanced scalability of machine learning systems
* Optimized network utilization

==Commercial Applications==
Edge Computing: Enhancing the efficiency of distributed machine learning systems in edge computing environments

==Prior Art==
Further research can be conducted on cooperative training methods in distributed systems and edge computing environments.

==Frequently Updated Research==
Stay updated on advancements in cooperative training methods for neural networks in distributed systems.

==Questions about Cooperative Training Migration==
===How does cooperative training migration improve the efficiency of neural network training?===
Cooperative training migration allows for the seamless transfer of data checkpoints between edge servers, optimizing network utilization and improving training speed.

===What are the key challenges in implementing cooperative training migration in distributed systems?===
The main challenges include ensuring data consistency, minimizing latency during data transfer, and maintaining network stability during migration processes.


==Original Abstract Submitted==

Cooperative training migration is performed by training, cooperatively with a computational device through a network, the neural network model, creating, during the iterations of training, a data checkpoint, the data checkpoint including the gradient values and the weight values of the server partition, the loss value, and an optimizer state, receiving, during the iterations of training, a migration notice, the migration notice including an identifier of a second edge server, and transferring, during the iterations of training, the data checkpoint to the second edge server.



[[Category:G06N3/08]]
[[Category:CPC_G06N3/08]]