Federated Learning Market Size, Share & Industry Trends Analysis Report By Application, By Vertical, By Regional Outlook and Forecast, 2022

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.

Application Outlook

Based on Application, the market is segmented into Drug Discovery, Risk Management, Online Visual Object Detection, Data Privacy & Security Management, Industrial Internet of Things, Augmented Reality/Virtual Reality, Shopping Experience Personalization, and Others. In 2021, the industrial internet of thigs segment procured a substantial revenue share of the federated learning market. Sensors are used in modern IoT networks, like wearable gadgets, autonomous vehicles, and smart homes, to gather and respond to the data in real-time. To operate properly, a fleet of autonomous vehicles, for example, may require an updated model of construction, traffic, or pedestrian behavior. Due to privacy concerns and the restricted connectivity of each device, constructing aggregate models in these cases may be challenging. Federated learning approaches make it possible to train models that can respond to changes in these systems quickly while respecting users' privacy. This factor is segmenting the growth of this segment.

Vertical Outlook

Based on Vertical, the market is segmented into Healthcare & Life Sciences, BFSI, IT & Telecommunication, Energy & Utilities, Manufacturing, Automotive & Transportation, Retail & Ecommerce, and Others. In 2021, the healthcare & life sciences segment witnessed the largest revenue share of the federated learning market. The increasing growth of this segment is attributed to the fact that healthcare and life sciences industry is constantly under pressure to improve the quality of services it provides to people. The amount of unstructured data in the healthcare industry is increasing significantly. Access to unstructured data, like medical device output, imaging reports, and lab findings, is ineffective in improving patient health. Pharmaceutical firms are included in the healthcare and life sciences category. With many research initiatives, consortiums, and implementations, the utilization of federated learning technologies is expediting in the healthcare and life sciences sectors.

Regional Outlook

Based on Regions, the market is segmented into North America, Europe, Asia Pacific, and Latin America, Middle East & Africa. In 2021, Europe accounted for the largest revenue share of the federated learning market. Patient data and risk analysis, precision medicine, lifestyle management, and monitoring, medical imaging and diagnostics, drug development, virtual assistant, inpatient care and hospital management, wearable, and research are some of the applications in the federated learning industry for healthcare. The drug development process is time-consuming, as it necessitates the analysis of massive amounts of bioscience data, such as patents, genetic data, and a significant number of papers uploaded daily throughout all biomedical journals as well as databases.

The major strategies followed by the market participants are Product Launches. Based on the Analysis presented in the Cardinal matrix; Microsoft Corporation and Google, Inc. are the forerunners in the Federated Learning Market. Companies such as Nvidia Corporation, IBM are some of the key innovators in the Market.

The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include IBM Corporation, Microsoft Corporation, Intel Corporation, Google LLC, Cloudera, Inc., NVIDIA Corporation, Edge Delta, Inc., DataFleets Ltd. (LiveRamp Holdings, Inc.), Enveil, and Secure AI Labs, Inc.

» Partnerships, Collaborations and Agreements:

Apr-2021: Cloudera joined hands with NVIDIA and Apache Spark 3.0. This collaboration aimed to enable scalable and accelerated big data workflows and pre-processing without code changes with the incorporation of the RAPIDS Accelerator for Apache Spark 3.0 along with NVIDIA's computing expertise into its Cloudera Data Platform.
May-2020: Intel collaborated with the University of Pennsylvania, a private Ivy League research university in Philadelphia. This collaboration aimed to offer a federation of 30 institutes to leverage federated learning in order to train AI models to detect the boundaries of brain tumors.

» Product Launches and Product Expansions:

Dec-2021: Nvidia rolled out FLARE, an open-source software platform. FLARE, or Federated Learning Application Runtime Environment, aimed to provide a mutual computing foundation for federated learning. Moreover, the new solution is expected to also underpin Clara Train’s federated learning software.
Nov-2021: Google introduced federated learning in its Smart Text Selection. With this launch, the company aimed to facilitate the process of training the neural network model across user interactions with increased reliability and user privacy. In addition, new improvements is expected to enable the models to be trained on-device on real interactions by leveraging federated learning.
Oct-2021: Google unveiled FedJAX, an open-source library based on JAX. This launch aimed to expedite and streamline the process of developing and evaluating federated algorithms. Moreover, the new solution is expected to also work as simple building blocks for the deployment of federated algorithms, models, prepackaged datasets, and faster simulation speed.
Jul-2021: Edge Delta launched an open demo environment. The new solution aimed to enable users to freely explore a fully functional environment, real-time insights being generated, and the value of the live continuous streaming data-based platform without the requirement for payment details and login credentials.
2021-May-2021: NVIDIA unveiled Clara Train 4.0, an application framework. This launch aimed to offer a foundation for medical imaging, which comprises AI-Assisted Annotation, Federated Learning, AI-Assisted Annotation, and AutoML. In addition, Clara Train is expected to also strengthen the company's underlying infrastructure from TensorFlow to MONAI.
Apr-2021: IBM introduced new capabilities into its IBM Watson. Through this product expansion, the company aimed to expand Watson tools, which are developed in order to aid enterprises in explaining and governing AI-led decisions. Moreover, the new capabilities is expected to also allow businesses to increase insight precision and minimize risks in order to fulfill their compliance and privacy requirements.
Jul-2020: IBM introduced IBM Federated Learning on Github. With this launch, the company aimed to offer a framework to its customers in order to enable them to boost their model training through the data aggregated from several sources while maintaining data privacy.
Apr-2020:Enveil rolled out ZeroReveal, an encrypted machine learning product. The new product is expected to allow businesses to process data against authenticated machine learning model. In addition, the new Enveil ZeroReveal ML is built on its ZeroReveal Search solution and is expected to change the secure data usage model by enabling businesses to experience advanced decision making via collaborative and federated machine learning with more privacy and security.

Scope of the Study

Market Segments Covered in the Report:

By Application

Drug Discovery
Risk Management
Online Visual Object Detection
Data Privacy & Security Management
Industrial Internet of Things
Augmented Reality/Virtual Reality
Shopping Experience Personalization
Others

By Vertical

Healthcare & Life Sciences
BFSI
IT & Telecommunication
Energy & Utilities
Manufacturing
Automotive & Transportation
Retail & Ecommerce
Others

By Geography

North America
US
Canada
Mexico
Rest of North America
Europe
Germany
UK
France
Russia
Spain
Italy
Rest of Europe
Asia Pacific
China
Japan
India
South Korea
Singapore
Malaysia
Rest of Asia Pacific
LAMEA
Brazil
Argentina
UAE
Saudi Arabia
South Africa
Nigeria
Rest of LAMEA

Key Market Players

List of Companies Profiled in the Report:

IBM Corporation
Microsoft Corporation
Intel Corporation
Google LLC
Cloudera, Inc.
NVIDIA Corporation
Edge Delta, Inc.
DataFleets Ltd. (LiveRamp Holdings, Inc.)
Enveil
Secure AI Labs, Inc.

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Chapter 1. Market Scope & Methodology

1.1 Market Definition
1.2 Objectives
1.3 Market Scope
1.4 Segmentation
1.4.1 Global Federated Learning Market, by Application
1.4.2 Global Federated Learning Market, by Vertical
1.4.3 Global Federated Learning Market, by Geography
1.5 Methodology for the research

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

6.1 North America Federated Learning Market
6.1.1 North America Federated Learning Market by Application
6.1.1.1 North America Drug Discovery Market by Country
6.1.1.2 North America Risk Management Market by Country
6.1.1.3 North America Online Visual Object Detection Market by Country
6.1.1.4 North America Data Privacy & Security Management Market by Country
6.1.1.5 North America Industrial Internet of Things Market by Country
6.1.1.6 North America Augmented Reality/Virtual Reality Market by Country
6.1.1.7 North America Shopping Experience Personalization Market by Country
6.1.1.8 North America Other Application Market by Country
6.1.2 North America Federated Learning Market by Vertical
6.1.2.1 North America Healthcare & Life Sciences Market by Country
6.1.2.2 North America BFSI Market by Country
6.1.2.3 North America IT & Telecommunication Market by Country
6.1.2.4 North America Energy & Utilities Market by Country
6.1.2.5 North America Manufacturing Market by Country
6.1.2.6 North America Automotive & Transportation Market by Country
6.1.2.7 North America Retail & Ecommerce Market by Country
6.1.2.8 North America Others Market by Country
6.1.3 North America Federated Learning Market by Country
6.1.3.1 US Federated Learning Market
6.1.3.1.1 US Federated Learning Market by Application
6.1.3.1.2 US Federated Learning Market by Vertical
6.1.3.2 Canada Federated Learning Market
6.1.3.2.1 Canada Federated Learning Market by Application
6.1.3.2.2 Canada Federated Learning Market by Vertical
6.1.3.3 Mexico Federated Learning Market
6.1.3.3.1 Mexico Federated Learning Market by Application
6.1.3.3.2 Mexico Federated Learning Market by Vertical
6.1.3.4 Rest of North America Federated Learning Market
6.1.3.4.1 Rest of North America Federated Learning Market by Application
6.1.3.4.2 Rest of North America Federated Learning Market by Vertical
6.2 Europe Federated Learning Market
6.2.1 Europe Federated Learning Market by Application
6.2.1.1 Europe Drug Discovery Market by Country
6.2.1.2 Europe Risk Management Market by Country
6.2.1.3 Europe Online Visual Object Detection Market by Country
6.2.1.4 Europe Data Privacy & Security Management Market by Country
6.2.1.5 Europe Industrial Internet of Things Market by Country
6.2.1.6 Europe Augmented Reality/Virtual Reality Market by Country
6.2.1.7 Europe Shopping Experience Personalization Market by Country
6.2.1.8 Europe Other Application Market by Country
6.2.2 Europe Federated Learning Market by Vertical
6.2.2.1 Europe Healthcare & Life Sciences Market by Country
6.2.2.2 Europe BFSI Market by Country
6.2.2.3 Europe IT & Telecommunication Market by Country
6.2.2.4 Europe Energy & Utilities Market by Country
6.2.2.5 Europe Manufacturing Market by Country
6.2.2.6 Europe Automotive & Transportation Market by Country
6.2.2.7 Europe Retail & Ecommerce Market by Country
6.2.2.8 Europe Others Market by Country
6.2.3 Europe Federated Learning Market by Country
6.2.3.1 Germany Federated Learning Market
6.2.3.1.1 Germany Federated Learning Market by Application
6.2.3.1.2 Germany Federated Learning Market by Vertical
6.2.3.2 UK Federated Learning Market
6.2.3.2.1 UK Federated Learning Market by Application
6.2.3.2.2 UK Federated Learning Market by Vertical
6.2.3.3 France Federated Learning Market
6.2.3.3.1 France Federated Learning Market by Application
6.2.3.3.2 France Federated Learning Market by Vertical
6.2.3.4 Russia Federated Learning Market
6.2.3.4.1 Russia Federated Learning Market by Application
6.2.3.4.2 Russia Federated Learning Market by Vertical
6.2.3.5 Spain Federated Learning Market
6.2.3.5.1 Spain Federated Learning Market by Application
6.2.3.5.2 Spain Federated Learning Market by Vertical
6.2.3.6 Italy Federated Learning Market
6.2.3.6.1 Italy Federated Learning Market by Application
6.2.3.6.2 Italy Federated Learning Market by Vertical
6.2.3.7 Rest of Europe Federated Learning Market
6.2.3.7.1 Rest of Europe Federated Learning Market by Application
6.2.3.7.2 Rest of Europe Federated Learning Market by Vertical
6.3 Asia Pacific Federated Learning Market
6.3.1 Asia Pacific Federated Learning Market by Application
6.3.1.1 Asia Pacific Drug Discovery Market by Country
6.3.1.2 Asia Pacific Risk Management Market by Country
6.3.1.3 Asia Pacific Online Visual Object Detection Market by Country
6.3.1.4 Asia Pacific Data Privacy & Security Management Market by Country
6.3.1.5 Asia Pacific Industrial Internet of Things Market by Country
6.3.1.6 Asia Pacific Augmented Reality/Virtual Reality Market by Country
6.3.1.7 Asia Pacific Shopping Experience Personalization Market by Country
6.3.1.8 Asia Pacific Other Application Market by Country
6.3.2 Asia Pacific Federated Learning Market by Vertical
6.3.2.1 Asia Pacific Healthcare & Life Sciences Market by Country
6.3.2.2 Asia Pacific BFSI Market by Country
6.3.2.3 Asia Pacific IT & Telecommunication Market by Country
6.3.2.4 Asia Pacific Energy & Utilities Market by Country
6.3.2.5 Asia Pacific Manufacturing Market by Country
6.3.2.6 Asia Pacific Automotive & Transportation Market by Country
6.3.2.7 Asia Pacific Retail & Ecommerce Market by Country
6.3.2.8 Asia Pacific Others Market by Country
6.3.3 Asia Pacific Federated Learning Market by Country
6.3.3.1 China Federated Learning Market
6.3.3.1.1 China Federated Learning Market by Application
6.3.3.1.2 China Federated Learning Market by Vertical
6.3.3.2 Japan Federated Learning Market
6.3.3.2.1 Japan Federated Learning Market by Application
6.3.3.2.2 Japan Federated Learning Market by Vertical
6.3.3.3 India Federated Learning Market
6.3.3.3.1 India Federated Learning Market by Application
6.3.3.3.2 India Federated Learning Market by Vertical
6.3.3.4 South Korea Federated Learning Market
6.3.3.4.1 South Korea Federated Learning Market by Application
6.3.3.4.2 South Korea Federated Learning Market by Vertical
6.3.3.5 Singapore Federated Learning Market
6.3.3.5.1 Singapore Federated Learning Market by Application
6.3.3.5.2 Singapore Federated Learning Market by Vertical
6.3.3.6 Malaysia Federated Learning Market
6.3.3.6.1 Malaysia Federated Learning Market by Application
6.3.3.6.2 Malaysia Federated Learning Market by Vertical
6.3.3.7 Rest of Asia Pacific Federated Learning Market
6.3.3.7.1 Rest of Asia Pacific Federated Learning Market by Application
6.3.3.7.2 Rest of Asia Pacific Federated Learning Market by Vertical
6.4 LAMEA Federated Learning Market
6.4.1 LAMEA Federated Learning Market by Application
6.4.1.1 LAMEA Drug Discovery Market by Country
6.4.1.2 LAMEA Risk Management Market by Country
6.4.1.3 LAMEA Online Visual Object Detection Market by Country
6.4.1.4 LAMEA Data Privacy & Security Management Market by Country
6.4.1.5 LAMEA Industrial Internet of Things Market by Country
6.4.1.6 LAMEA Augmented Reality/Virtual Reality Market by Country
6.4.1.7 LAMEA Shopping Experience Personalization Market by Country
6.4.1.8 LAMEA Other Application Market by Country
6.4.2 LAMEA Federated Learning Market by Vertical
6.4.2.1 LAMEA Healthcare & Life Sciences Market by Country
6.4.2.2 LAMEA BFSI Market by Country
6.4.2.3 LAMEA IT & Telecommunication Market by Country
6.4.2.4 LAMEA Energy & Utilities Market by Country
6.4.2.5 LAMEA Manufacturing Market by Country
6.4.2.6 LAMEA Automotive & Transportation Market by Country
6.4.2.7 LAMEA Retail & Ecommerce Market by Country
6.4.2.8 LAMEA Others Market by Country
6.4.3 LAMEA Federated Learning Market by Country
6.4.3.1 Brazil Federated Learning Market
6.4.3.1.1 Brazil Federated Learning Market by Application
6.4.3.1.2 Brazil Federated Learning Market by Vertical
6.4.3.2 Argentina Federated Learning Market
6.4.3.2.1 Argentina Federated Learning Market by Application
6.4.3.2.2 Argentina Federated Learning Market by Vertical
6.4.3.3 UAE Federated Learning Market
6.4.3.3.1 UAE Federated Learning Market by Application
6.4.3.3.2 UAE Federated Learning Market by Vertical
6.4.3.4 Saudi Arabia Federated Learning Market
6.4.3.4.1 Saudi Arabia Federated Learning Market by Application
6.4.3.4.2 Saudi Arabia Federated Learning Market by Vertical
6.4.3.5 South Africa Federated Learning Market
6.4.3.5.1 South Africa Federated Learning Market by Application
6.4.3.5.2 South Africa Federated Learning Market by Vertical
6.4.3.6 Nigeria Federated Learning Market
6.4.3.6.1 Nigeria Federated Learning Market by Application
6.4.3.6.2 Nigeria Federated Learning Market by Vertical
6.4.3.7 Rest of LAMEA Federated Learning Market
6.4.3.7.1 Rest of LAMEA Federated Learning Market by Application
6.4.3.7.2 Rest of LAMEA Federated Learning Market by Vertical

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

Companies Mentioned

IBM Corporation
Microsoft Corporation
Intel Corporation
Google LLC
Cloudera, Inc.
NVIDIA Corporation
Edge Delta, Inc.
DataFleets Ltd. (LiveRamp Holdings, Inc.)
Enveil
Secure AI Labs, Inc.

Methodology

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Table Information

Report Attribute	Details
No. of Pages	244
Published	May 2022
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
No. of Companies Mentioned	10

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Federated Learning Market Size, Share & Industry Trends Analysis Report By Application, By Vertical, By Regional Outlook and Forecast, 2022 - 2028