Insights on the Federated Learning Global Market to 2028 – Enhanced Data Privacy in Numerous Applications is Driving Growth

0
10

Dublin, Nov. 22, 2022 (GLOBE NEWSWIRE) — 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.

The Global Federated Learning Market size is expected to reach $198.7 Million by 2028, rising at a market growth of 11.1% CAGR during the forecast period.

Federated learning can be described as a machine learning approach that distributes an algorithm among a number of decentralized end devices or servers that each have local data samples. This strategy differs from standard centralized machine learning methods, which store all local datasets on a single server. Additionally, this technique ensures that the local data samples are disseminated to the server in the same way.

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.

The growing need for improved data protection and privacy, as well as the increasing requirement to adapt data in real-time to optimize conversions automatically are driving the advancement of the federated learning solutions market. Moreover, by retaining data on devices, these solutions assist organizations in leveraging machine learning models, boosting the federated learning market forward.

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 efforts 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.

Report Attribute Details
No. of Pages 244
Forecast Period 2021 – 2028
Estimated Market Value (USD) in 2021 $98 Million
Forecasted Market Value (USD) by 2028 $199 Million
Compound Annual Growth Rate 11.1%
Regions Covered Global

Key Topics Covered:

Chapter 1. Market Scope & Methodology

Chapter 2. Market Overview
2.1 Introduction
2.1.1 Overview
2.1.1.1 Market Composition and Scenario
2.2 Key Factors Impacting the Market
2.2.1 Market Drivers
2.2.2 Market Restraints

Chapter 3. Competition Analysis – Global
3.1 KBV Cardinal Matrix
3.2 Recent Industry Wide Strategic Developments
3.2.1 Partnerships, Collaborations and Agreements
3.2.2 Product Launches and Product Expansions
3.2.3 Acquisition and Mergers
3.3 Top Winning Strategies
3.3.1 Key Leading Strategies: Percentage Distribution (2018-2022)
3.3.2 Key Strategic Move: (Product Launches and Product Expansions : 2018, Dec – 2021, Dec) Leading Players

Chapter 4. Global Federated Learning Market by Application
4.1 Global Drug Discovery Market by Region
4.2 Global Risk Management Market by Region
4.3 Global Online Visual Object Detection Market by Region
4.4 Global Data Privacy & Security Management Market by Region
4.5 Global Industrial Internet of Things Market by Region
4.6 Global Augmented Reality/Virtual Reality Market by Region
4.7 Global Shopping Experience Personalization Market by Region
4.8 Global Other Application Market by Region

Chapter 5. Global Federated Learning Market by Vertical
5.1 Global Healthcare & Life Sciences Market by Region
5.2 Global BFSI Market by Region
5.3 Global IT & Telecommunication Market by Region
5.4 Global Energy & Utilities Market by Region
5.5 Global Manufacturing Market by Region
5.6 Global Automotive & Transportation Market by Region
5.7 Global Retail & Ecommerce Market by Region
5.8 Global Others Market by Region

Chapter 6. Global Federated Learning Market by Region

Chapter 7. Company Profiles
7.1 IBM Corporation
7.1.1 Company Overview
7.1.2 Financial Analysis
7.1.3 Regional & Segmental Analysis
7.1.4 Research & Development Expenses
7.1.5 Recent Strategies and Developments
7.1.5.1 Product Launches and Product Expansions:
7.2 Microsoft Corporation
7.2.1 Company Overview
7.2.2 Financial Analysis
7.2.3 Segmental and Regional Analysis
7.2.4 Research & Development Expenses
7.2.5 Recent Strategies and Developments
7.2.5.1 Product Launches and Product Expansions:
7.2.5.2 Acquisitions and Mergers:
7.3 Intel Corporation
7.3.1 Company Overview
7.3.2 Financial Analysis
7.3.3 Segmental and Regional Analysis
7.3.4 Research & Development Expenses
7.3.5 Recent strategies and developments:
7.3.5.1 Partnerships, Collaborations and Agreement:
7.4 Google LLC
7.4.1 Company Overview
7.4.2 Financial Analysis
7.4.3 Segmental and Regional Analysis
7.4.4 Research & Development Expense
7.4.5 Recent Strategies and Developments
7.4.5.1 Product Launches and Product Expansions:
7.5.5 Recent strategies and developments:
7.5.5.1 Partnerships, Collaborations and Agreements:
7.6 NVIDIA Corporation
7.6.1 Company Overview
7.6.2 Financial Analysis
7.6.3 Segmental and Regional Analysis
7.6.4 Research & Development Expense
7.6.5 Recent strategies and developments:
7.6.5.1 Partnerships, Collaborations and Agreements:
7.6.6 SWOT Analysis
7.7 Edge Delta, Inc.
7.7.1 Company Overview
7.7.2 Recent strategies and developments:
7.7.2.1 Product Launches and Product Expansions:
7.8 DataFleets Ltd. (LiveRamp Holdings, Inc.)
7.8.1 Company Overview
7.9 Enveil
7.9.1 Company Overview
7.9.2 Recent strategies and developments:
7.9.2.1 Product Launches and Product Expansions:
7.10. Secure AI Labs, Inc.
7.10.1 Company Overview

For more information about this report visit https://www.researchandmarkets.com/r/3xkp8q

Attachment