The Worldwide Federated Learning Industry is Expected to Reach $198.7 Million by 2028 at a 11.1% CAGR
DUBLIN, Dec. 1, 2022 /PRNewswire/ -- The "Global Federated Learning Market Size, Share & Industry Trends Analysis Report By Application, By Vertical, By Regional Outlook and Forecast, 2022 - 2028" report has been added to ResearchAndMarkets.com's offering.
Federated learning can be utilized to build consumer behavior models from the data pool of smartphones without revealing personal information, like for next-word prediction, voice recognition, facial identification, and other applications. Federated learning enables various vendors to develop a shared machine learning algorithm without sharing data, allowing crucial issues like data access rights, data privacy and security, and the capacity to access heterogeneous data to be addressed. Defense, telecommunications, and medicines are among the businesses that can leverage federated learning to optimize their operations.
Furthermore, the ability to provide predictive features on the latest smart devices without compromising the consumer experience or divulging private information is providing lucrative opportunities for the federated learning market to develop throughout the coming years.
COVID-19 Impact Analysis
COVID-19 is an unprecedented global public health crisis that has impacted practically every business, and its long-term repercussions significantly impacted various markets in numerous countries all over the world. In addition, governments across the world imposed lockdown in their countries in order to regulate the diffusion of the hazardous COVID-19 infection.
These lockdowns caused major disruptions in the worldwide supply chain of all the products and services due to travel restrictions. The infection was rapidly spreading all over the world, creating economic stagnation and compelling thousands of employees to work from home. However, artificial intelligence, as well as machine learning, were majorly used to forecast and investigate the spread of potential data alarms in several countries all over the world.
Market Growth Factors
Enhanced data privacy in numerous applications
Due to federated learning, the manner in which ML approaches are offered is evolving. Companies are increasing their eforts on performing a thorough investigation of federated learning. Using federated learning, companies may reinforce their existing algorithms and improve their AI applications.
The demand for improved learning is increasing among both gadgets and companies. In the healthcare field, federated learning could help healthcare personnel deliver high-quality outcomes while also accelerating drug development. For example, FADNet, a new peer-to-peer technique, is a remedy for centralized learning inadequacies.
Enables collaborative learning among various users
Federated learning, rather than keeping data on a single computer or data mart, stores data on original sources, like smartphones, manufacturing detection equipment, other end devices, and machine learning machines are trained on the go. This aids in decision-making before being sent back to a centralized computer. For example, federated learning is widely used in the finance sector for debt risk assessments.
Typically, banks use whitelisting processes to keep customers out of the Federal Reserve System based on their credit card information. Risk assessment variables, like taxation and reputation, may be employed by working with other financial institutions and eCommerce businesses.
Market Restraining Factors
Scarcity of skilled technical professionals
Many businesses encounter a significant impediment when integrating machine learning into existing workflows due to a scarcity of trained people, particularly IT specialists. Because federated learning systems are a new concept, it is difficult for personnel to grasp and execute them.
Recruiting and maintaining technical skills became a major concern for several firms due to a scarcity of skilled candidates to incorporate federated learning projects that include difficult methodologies, such as machine learning. As an organization, they must develop a growing range of talents and job titles. Organizations, for example, require experts that can administer and comprehend the current federated learning architecture connected with the installation and maintenance of machine learning algorithms.
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