Breakthrough in Wireless Devices Market: Researchers Develop a Technique that can help with Excess Transport of Data between Wireless Networks

• Analysis
• 03-March-2022

Federated learning is a terrific technique for teaching AI systems while also safeguarding data privacy. However, the amount of data traffic required has made it unmanageable for systems with wireless devices.
 
The new technique employs compression to reduce the bulk of data streams significantly. This is a massive opportunity for Industrial Wireless Devices Market as it opens up new possibilities for AI training on wireless technology.
 
Federated learning refers to a type of machine learning that involves several clients or devices. Each client is given distinct data and creates its model for completing a specific task. After that, the clients send away their models to a central server. The present method allows wireless devices with limited bandwidth to participate in federated learning.
 
The centralized server then combines all the models to create a hybrid model that outperforms any individual models. The hybrid model is sent back to each client via the central server thereafter. The procedure is repeated several times, with each iteration resulting in model improvements. The idea is to increase the system's performance in total.
 
One of the benefits associated with federated learning is that it improves the overall AI system's performance. It also does so without jeopardizing the privacy of the data used to train it. For example, to develop diagnostic AI technologies, one may use confidential patient data from several hospitals without having access to each other's patients' data.
 
Many jobs could be accomplished by utilizing information saved on people's personal devices, such as cell phones. Federated learning could be one way of putting that data to good use without violating any privacy principles. However, the learning algorithm is not without faults. During training, federated learning requires a great deal of communication between the clients and the central server when they send model updates. This continuous communication might clog wireless connections in places with limited bandwidth or considerable data traffic, slowing the operation.
 
The researchers devised a technology that specifically helped clients to compress data into substantially smaller packets. Before being sent, the packages are compressed and then reassembled by the centralized server. A set of algorithms devised by the study team allows the process to occur. Using this method, the researchers reduced the amount of wireless data sent from clients by up to 99 percent.
 
The approach and its applications are incredible, and once they are introduced in the market, they could revolutionize the whole wireless devices industry.
 
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