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Global Federated Learning Solutions Market to reach USD 257.03 million by the end of 2030.

Global Federated Learning Solutions Market Size study & Forecast, by Application (Drug Discovery, Shopping Experience Personalization, Data Privacy and Security Management, Risk Management, Industrial Internet of Things, Online Visual Object Detection, Augmented Reality/Virtual Reality, Other), by Verticals (Banking, Financial Services, and Insurance, Healthcare and Life Sciences, Retail and Ecommerce, Manufacturing, Energy and Utilities, Automotive and Transportation, IT and Telecommunication, Other) and Regional Analysis, 2023-2030

Product Code: ICTICTI-17728279
Publish Date: 10-09-2023
Page: 200

Global Federated Learning Solutions Market is valued at approximately USD 114.8 million in 2022 and is anticipated to grow with a healthy growth rate of more than 10.6% over the forecast period 2023-2030. Federated Learning is a machine learning approach that trains a centralized model using data distributed across numerous clients with unreliable and slow network connections. It enables training algorithms on decentralized edge devices or servers without sharing the underlying data. The Federated Learning Solutions Market is expanding because of factors such as the rising adoption of the Internet of Things and rising demand of Edge Computing. Moreover, the rising adoption of edge computing positively impacts the Federated Learning Solutions market by enabling real-time decision-making and reducing the need for data transfer, enhancing privacy and security.

According to Statista in 2022, The global edge computing is expected to reach approximately USD 274 billion by the year 2025. The rising adoption of IoT devices generates huge amounts of distributed data, driving the need for federated learning solutions that enable secure and efficient model training across these devices. This trend contributes to the growth and expansion of the Federated Learning Solutions Market. According to Statista in 2022, the number of Internet of Things (IoT) devices worldwide is expected to nearly triple from USD 9.7 billion in 2020 to over USD 29 billion by 2030. By 2030, China is projected to have the largest number of IoT devices, with around 5 billion devices used by consumers. In addition, increasing demand for secure and efficient data analysis and technological advancements in federated learning solutions would create a lucrative growth opportunity. However, a lack of skilled technical expertise and data privacy and security concerns stifles market growth throughout the forecast period of 2023-2030.

The key regions considered for the Global Federated Learning Solutions Market study includes Asia Pacific, North America, Europe, Latin America, and Middle East & Africa. North America dominated the market during the forecast period owing to the, rising adoption of federated learning solutions driven by stringent data regulations, privacy concerns, and technological advancements and the growing use of innovative technologies such as Artificial intelligence and big data analytics increase market growth in the region. Asia Pacific is the fastest growing region during the forecast period owing to the factors such as the rising utilization of mobile devices and IoT, along with increase adoption of advanced technologies in this region.

Major market player included in this report are:
Intellegens Ltd.
Microsoft Corporation
Intel Corporation
Cloudera Inc,
Edge Delta Inc.,
International Business Machines Corporation,
Enveil Inc.,
DataFleets Ltd,
Alphabet Inc,
Nvidia Corporation

Recent Developments in the Market:
Ø In March 2022, The Communications Intelligence Platform, a Clara Holoscan solution created by NVIDIA, has now been upgraded to MGX. This one-of-a-kind system provides end-to-end capabilities for AI technologies, intelligent healthcare manufacturing, and implantable augmentation deployment.

Ø In June 2022 Intel Corporation collaborated with Aster Innovation and Research Centre and CARPL, to create a secure federated education platform. This alliance seeks to promote innovation in a variety of fields, including diagnosis, genomics, drug discovery, and predictive healthcare.

Global Federated Learning Solutions Market Report Scope:
ü Historical Data – 2020 – 2021
ü Base Year for Estimation – 2022
ü Forecast period – 2023-2030
ü Report Coverage – Revenue forecast, Company Ranking, Competitive Landscape, Growth factors, and Trends
ü Segments Covered – Application, Verticals, Region
ü Regional Scope – North America; Europe; Asia Pacific; Latin America; Middle East & Africa
ü Customization Scope – Free report customization (equivalent up to 8 analyst’s working hours) with purchase. Addition or alteration to country, regional & segment scope*

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within countries involved in the study.

The report also caters detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, it also incorporates potential opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:

By Application:
Drug Discovery
Shopping Experience Personalization
Data Privacy and Security Management
Risk Management
Industrial Internet of Things
Online Visual Object Detection
Augmented Reality/Virtual Reality
Other

By Verticals:
Banking, Financial Services, and Insurance
Healthcare and Life Sciences
Retail and Ecommerce
Manufacturing
Energy and Utilities
Automotive and Transportation
IT and Telecommunication
Other

By Region:

North America
U.S.
Canada

Europe
UK
Germany
France
Spain
Italy
ROE

Asia Pacific
China
India
Japan
Australia
South Korea
RoAPAC

Latin America
Brazil
Mexico

Middle East & Africa
Saudi Arabia
South Africa
Rest of Middle East & Africa

Chapter 1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2020-2030 (USD Million)
1.2.1. Federated Learning Solutions Market, by Region, 2020-2030 (USD Million)
1.2.2. Federated Learning Solutions Market, by Application, 2020-2030 (USD Million)
1.2.3. Federated Learning Solutions Market, by Verticals, 2020-2030 (USD Million)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global Federated Learning Solutions Market Definition and Scope
2.1. Objective of the Study
2.2. Market Definition & Scope
2.2.1. Industry Evolution
2.2.2. Scope of the Study
2.3. Years Considered for the Study
2.4. Currency Conversion Rates
Chapter 3. Global Federated Learning Solutions Market Dynamics
3.1. Federated Learning Solutions Market Impact Analysis (2020-2030)
3.1.1. Market Drivers
3.1.1.1. Rising adoption of Internet of Things (IoT)
3.1.1.2. Rising demand of Edge Computing
3.1.2. Market Challenges
3.1.2.1. Lack of skilled technical expertise
3.1.2.2. Data privacy and security concerns
3.1.3. Market Opportunities
3.1.3.1. Increasing demand for secure and efficient data analysis
3.1.3.2. Technological advancements in federated learning solutions
Chapter 4. Global Federated Learning Solutions Market Industry Analysis
4.1. Porter’s 5 Force Model
4.1.1. Bargaining Power of Suppliers
4.1.2. Bargaining Power of Buyers
4.1.3. Threat of New Entrants
4.1.4. Threat of Substitutes
4.1.5. Competitive Rivalry
4.2. Porter’s 5 Force Impact Analysis
4.3. PEST Analysis
4.3.1. Political
4.3.2. Economical
4.3.3. Social
4.3.4. Technological
4.3.5. Environmental
4.3.6. Legal
4.4. Top investment opportunity
4.5. Top winning strategies
4.6. COVID-19 Impact Analysis
4.7. Disruptive Trends
4.8. Industry Expert Perspective
4.9. Analyst Recommendation & Conclusion
Chapter 5. Global Federated Learning Solutions Market, by Application
5.1. Market Snapshot
5.2. Global Federated Learning Solutions Market by Application, Performance – Potential Analysis
5.3. Global Federated Learning Solutions Market Estimates & Forecasts by Application2020-2030 (USD Million)
5.4. Federated Learning Solutions Market, Sub Segment Analysis
5.5. Drug Discovery
5.6. Shopping Experience Personalization
5.7. Data Privacy and Security Management
5.8. Risk Management
5.9. Industrial Internet of Things
5.10. Online Visual Object Detection
5.11. Augmented Reality/Virtual Reality
5.12. Other

Chapter 6. Global Federated Learning Solutions Market, by Verticals
6.1. Market Snapshot
6.2. Global Federated Learning Solutions Market by Verticals, Performance – Potential Analysis
6.3. Global Federated Learning Solutions Market Estimates & Forecasts by Verticals2020-2030 (USD Million)
6.4. Federated Learning Solutions Market, Sub Segment Analysis
6.5. Banking, Financial Services, and Insurance
6.6. Healthcare and Life Sciences
6.7. Retail and Ecommerce
6.8. Manufacturing
6.9. Energy and Utilities
6.10. Automotive and Transportation
6.11. IT and Telecommunication
6.12. Other

Chapter 7. Global Federated Learning Solutions Market, Regional Analysis
7.1. Top Leading Countries
7.2. Top Emerging Countries
7.3. Federated Learning Solutions Market, Regional Market Snapshot
7.4. North America Federated Learning Solutions Market
7.4.1. U.S. Federated Learning Solutions Market
7.4.1.1. Application breakdown estimates & forecasts, 2020-2030
7.4.1.2. Verticals breakdown estimates & forecasts, 2020-2030
7.4.2. Canada Federated Learning Solutions Market
7.5. Europe Federated Learning Solutions Market Snapshot
7.5.1. U.K. Federated Learning Solutions Market
7.5.2. Germany Federated Learning Solutions Market
7.5.3. France Federated Learning Solutions Market
7.5.4. Spain Federated Learning Solutions Market
7.5.5. Italy Federated Learning Solutions Market
7.5.6. Rest of Europe Federated Learning Solutions Market
7.6. Asia-Pacific Federated Learning Solutions Market Snapshot
7.6.1. China Federated Learning Solutions Market
7.6.2. India Federated Learning Solutions Market
7.6.3. Japan Federated Learning Solutions Market
7.6.4. Australia Federated Learning Solutions Market
7.6.5. South Korea Federated Learning Solutions Market
7.6.6. Rest of Asia Pacific Federated Learning Solutions Market
7.7. Latin America Federated Learning Solutions Market Snapshot
7.7.1. Brazil Federated Learning Solutions Market
7.7.2. Mexico Federated Learning Solutions Market
7.8. Middle East & Africa Federated Learning Solutions Market
7.8.1. Saudi Arabia Federated Learning Solutions Market
7.8.2. South Africa Federated Learning Solutions Market
7.8.3. Rest of Middle East & Africa Federated Learning Solutions Market

Chapter 8. Competitive Intelligence
8.1. Key Company SWOT Analysis
8.1.1. Company 1
8.1.2. Company 2
8.1.3. Company 3
8.2. Top Market Strategies
8.3. Company Profiles
8.3.1. Intellegens Ltd.
8.3.1.1. Key Information
8.3.1.2. Overview
8.3.1.3. Financial (Subject to Data Availability)
8.3.1.4. Product Summary
8.3.1.5. Recent Developments
8.3.2. Microsoft Corporation
8.3.3. Intel Corporation
8.3.4. Cloudera Inc
8.3.5. Edge Delta Inc.
8.3.6. International Business Machines Corporation
8.3.7. Enveil Inc.
8.3.8. DataFleets Ltd
8.3.9. Alphabet Inc
8.3.10. Nvidia Corporation
Chapter 9. Research Process
9.1. Research Process
9.1.1. Data Mining
9.1.2. Analysis
9.1.3. Market Estimation
9.1.4. Validation
9.1.5. Publishing
9.2. Research Attributes
9.3. Research Assumption

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Data Collection:
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Data Analysis:
Our team of experts carefully examine the gathered data using suitable statistical techniques and qualitative analysis methods. For quantitative analysis, we employ descriptive statistics, regression analysis, and other advanced statistical methods, depending on the characteristics of the data. This analysis may also incorporate the utilization of AI tools and big data analysis techniques to extract meaningful insights.
To ensure the accuracy and reliability of our findings, we extensively leverage data science techniques, which help us minimize discrepancies and uncertainties in our analysis. We employ Data Science to clean and preprocess the data, ensuring its quality and reliability. This involves handling missing data, removing outliers, standardizing variables, and transforming data into suitable formats for analysis. The application of data science techniques enhances our accuracy, efficiency, and depth of analysis, enabling us to stay competitive in dynamic market environments.
Market Size Estimation:
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In our forecasting process, we consider various parameters such as economic tools, technological analysis, industry experience, and domain expertise. By taking all these factors into account, we strive to produce accurate and reliable market forecasts. When forecasting, we take into consideration several parameters, which include:
Market driving trends and favorable economic conditions
Restraints and challenges that are expected to be encountered during the forecast period.
Anticipated opportunities for growth and development
Technological advancements and projected developments in the market
Consumer spending trends and dynamics
Shifts in consumer preferences and behaviors.
The current state of raw materials and trends in supply versus pricing
Regulatory landscape and expected changes or developments.
The existing capacity in the market and any expected additions or expansions up to the end of the forecast period.
To assess the market impact of these parameters, we assign weights to each one and utilize weighted average analysis. This process allows us to quantify their influence on the market and derive an expected growth rate for the forecasted period. By considering these various factors and applying a weighted analysis approach, we strive to provide accurate and reliable market forecasts.
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