Insights on the Global Federated Learning Market to 2028 –

Dublin, November 22, 2022 (GLOBE NEWSWIRE) — The “Analysis Report of Global Federated Learning Market Size, Share and Industry Trends by Application, by Vertical, by Regional Outlook and Forecast, 2022 – 2028” report has been added to researchandmarkets.com offering.

The global federated learning market size is expected to reach $198.7 million by 2028, increasing 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 servers or end devices, each of which has local data samples. This strategy differs from standard centralized machine learning methods, which store all local data sets on a single server. Furthermore, this technique ensures that local data samples are broadcast to the server in the same way.

Federated learning can be used to model consumer behavior from the smartphone dataset without revealing personal information, such as next word prediction, voice recognition, facial identification, and other applications. Federated learning allows multiple vendors to develop a shared machine learning algorithm without sharing data, allowing crucial issues such as data access rights, data privacy and security, and the ability to access heterogeneous data to be addressed . Defense, telecommunications, and pharmaceuticals are among the companies that can take advantage of federated learning to optimize their operations.

The growing need to improve data protection and privacy, as well as the growing requirement to adapt data in real time to automatically optimize conversions, are driving the advancement of the federated learning solutions market. Additionally, by retaining data on devices, these solutions help organizations take advantage of machine learning models, driving the federated learning market.

Additionally, the ability to provide predictive features in the latest smart devices without compromising the consumer experience or disclosing private information is providing lucrative opportunities for federated learning market development in the coming years.

COVID-19 Impact Analysis

COVID-19 is an unprecedented global public health crisis that has affected virtually all businesses, with its long-term repercussions significantly affecting various markets in numerous countries around the world. In addition, governments around the world have imposed lockdowns on their countries to regulate the spread of the dangerous COVID-19 infection.

These lockdowns caused major disruptions in the global supply chain of all products and services due to travel restrictions. The infection was spreading rapidly across the globe, creating economic stagnation and forcing thousands of employees to work from home. However, artificial intelligence, as well as machine learning, was mainly used to forecast and investigate the spread of potential data alarms in various countries around the world.

Market growth factors

Improved data privacy in many applications

Due to federated learning, the way ML approaches are delivered is evolving. Companies are increasing their efforts to conduct comprehensive research on federated learning. Through federated learning, companies can strengthen their existing algorithms and improve their AI applications.

Demand for enhanced learning is increasing both across devices and across businesses. In healthcare, federated learning could help healthcare personnel deliver high-quality outcomes while accelerating drug development. For example, FADNet, a new peer-to-peer technique, is a remedy for the deficiencies of centralized learning.

It allows collaborative learning between several users.

Federated learning, instead of keeping data on a single computer or data mart, stores data in original sources such as smartphones, manufacturing sensing equipment, other end devices, and machine learning machines that are trained on the fly. This aids in decision making before being sent back to a centralized computer. For example, federated learning is widely used in the financial sector for debt risk assessments.

Banks typically use whitelisting processes to keep customers out of the Federal Reserve System based on their credit card information. Risk assessment variables, such as taxes and reputation, can be used by working with other financial institutions and e-commerce companies.

Market restraint factors

Shortage of qualified technical professionals

Many companies encounter a significant impediment in integrating machine learning into existing workflows due to a shortage of trained people, particularly IT specialists. Because federated learning systems are a new concept, they are difficult for staff to understand and implement.

Recruiting and retaining technical skills has become a major concern for several companies due to a shortage of qualified candidates to join federated learning projects that include difficult methodologies such as machine learning. As an organization, they must develop an ever-growing range of talent and job titles. Organizations, for example, require experts who can manage and understand the current federated learning architecture related to installing and maintaining machine learning algorithms.

report attribute Details
No. of pages 244
forecast period 2021 – 2028
Estimated Market Value (USD) in 2021 $98 million
Expected Market Value (USD) by 2028 $199 million
compound annual growth rate 11.1%
Regions covered Global

Key topics covered:

Chapter 1. Market Scope and Methodology

Chapter 2. Market Overview
2.1 Introduction
2.1.1 Summary
2.1.1.1 Market Composition and Scenario
2.2 Key factors affecting the market
2.2.1 Market drivers
2.2.2 Market restrictions

Chapter 3. Competitor Analysis – Global
3.1 KBV Cardinal Matrix
3.2 Recent strategic developments across the industry
3.2.1 Partnerships, Collaborations and Agreements
3.2.2 Product releases and product expansions
3.2.3 Acquisition and Mergers
3.3 Main winning strategies
3.3.1 Main main strategies: percentage distribution (2018-2022)
3.3.2 Key Strategic Move: (Product Launches and Product Expansions: December 2018 – December 2021) Major 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 and 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 Customer Service Personalization Market by Region
4.8 Global Other Applications Market by Region

Chapter 5 Global Federated Learning Market by Vertical
5.1 Global Healthcare and Life Sciences Market by Region
5.2 Global BFSI Market by Region
5.3 Global IT and Telecom Market by Region
5.4 Global Energy and Utilities Market by Region
5.5 Global Manufacturing Market by Region
5.6 Global Automotive and Transportation Market by Region
5.7 Global Retail and E-Commerce 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 and segmental analysis
7.1.4 Research and development expenses
7.1.5 Recent strategies and developments
7.1.5.1 Product Releases 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 and development expenses
7.2.5 Recent strategies and developments
7.2.5.1 Product Releases 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 and development expenses
7.3.5 Recent Strategies and Developments:
7.3.5.1 Partnerships, Collaborations and Agreements:
7.4 Google Limited Liability Company
7.4.1 Company overview
7.4.2 Financial analysis
7.4.3 Segmental and Regional Analysis
7.4.4 Research and development expenses
7.4.5 Recent strategies and developments
7.4.5.1 Product Releases 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 and development expenses
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 Releases and Product Expansions:
7.8 DataFleets Ltd. (LiveRamp Holdings, Inc.)
7.8.1 Company Overview
7.9 Wrap
7.9.1 Company Overview
7.9.2 Recent Strategies and Developments:
7.9.2.1 Product Releases and Product Expansions:
7.10. Secure AI Labs, Inc.
7.10.1 Company Overview

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


        

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