The Federated Learning Market size was valued at USD 113.2 Million in 2022. It is expected to hit USD 261.8 Million by 2030 and grow at a CAGR of 11.05% over the forecast period of 2023-2030.
The market is expected to experience revenue growth due to several factors. Firstly, there is a growing concern regarding data privacy and the storage of data in decentralized devices. This has led to an increased focus on finding solutions that can address these concerns effectively. Furthermore, the healthcare sector has witnessed a significant adoption of federated learning. This approach allows organizations to collaborate and share knowledge while maintaining data privacy. The benefits of this collaborative learning approach have been recognized, leading to its widespread implementation in the healthcare industry. there is a rising need to enhance learning capabilities between organizations and devices. This requirement has driven the demand for federated learning, as it enables seamless communication and knowledge transfer between different entities. The integration of advanced technologies, such as machine learning, with federated learning has further propelled the market's growth. This combination allows for more efficient and accurate data analysis, leading to improved decision-making processes.
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Federated learning, which trains, tests, and provides data security, is the new era of secure artificial intelligence (AI). Additionally, the introduction of such a technology makes it tough to hack data. The federated learning approach has enormous promise because it aids in protecting the private and sensitive data of individuals and organizations. The module is robust because it also aggregates results and detects familiar samples from different users.
Drivers
Increasing the use of federated learning for data privacy in numerous areas
To develop the industry, there is a need to improve learning between devices and organizations.
Machine learning has recently advanced and changed how technology is used. The majority of organizations are implementing cutting-edge technologies like AI, ML, and IoT to analyze data and extract useful insights. Information is growing as technology is used more and more, therefore protecting it calls for a high level of privacy. Many firms frequently employ federal learning to train their algorithms on different datasets without exchanging data. Federal learning permits on-device machine learning without sending the user's personal data to a centralized cloud, which helps to improve device performance in IoT applications and achieve personalization. For instance, Apple uses federated learning to enhance Siri's voice recognition, while Google's Android Keyboard uses federal learning technology to enhance predictive texts without sharing the user's sensitive data.
Restrains
Insufficient technical expertise
When ML is implemented into current workflows, many businesses encounter a substantial barrier due to a lack of competent people, particularly IT specialists. Due to the revolutionary nature of federated learning systems, it might be difficult for personnel to understand and implement them. Recruiting and maintaining technological talents became a major challenge for several firms due to a lack of qualified individuals to create and implement federated learning projects that require complex methodologies like machine learning. They must develop more job titles and skill sets as a company. For instance, organizations require engineers who can oversee and comprehend the contemporary federated learning architecture connected to the setup and upkeep of machine learning algorithms. With considerable knowledge in computer science, statistics, and conceptual understanding, data scientists are among the most highly educated scientific specialists. On the other hand, skilled data scientists demand expensive prices and products that are frequently beyond the means of small and medium-sized businesses or even huge corporations. The necessity to remain relevant in a market with constrained skills is driving an increase in the demand for federated learning modules across industries. As a result, a major obstacle for the global market for federated learning solutions is the current shortage of skilled workers.
Opportunities
Federated learning allows for the local storage of data while allowing distant users to learn jointly from a common model.
There has been a significant increase in the adoption of artificial intelligence (AI)-enabled federated education techniques and a rise in the level of vehicle automation.
Challenges
Issues with system interoperability and integration
If the conflict disrupts supply chains, particularly in the manufacturing and electronics sectors, it could affect the production and availability of hardware components necessary for Federated Learning systems. This could lead to delays and increased costs for companies implementing Federated Learning solutions.
Geopolitical tensions can lead to changes in export controls and regulations related to technology transfer. This might affect the ability of companies to share Federated Learning technologies and collaborate across borders. As Federated Learning relies on decentralized data processing and sharing, concerns about data security and privacy may increase in regions affected by the conflict. Companies might become more cautious about adopting Federated Learning solutions if they perceive increased risks. Universities and research institutions in regions directly affected by the conflict might see disruptions in their research activities. This could potentially slow down advancements in Federated Learning algorithms and techniques. Governments in conflict-affected regions might prioritize resources for defense and security rather than investing in AI research and development, which could indirectly impact the growth of the Federated Learning market. On the other hand, the conflict might lead to increased interest in technologies that enhance data security and privacy, potentially driving greater adoption of Federated Learning solutions.
Impact of Recession
Recessions often lead to cautious spending and a focus on core operations. Companies may be 32% less willing to experiment with emerging technologies like Federated Learning, which might slow down its adoption rate. Startups in the Federated Learning space may face challenges in securing funding during a recession. Investors may become more risk-averse, making it harder for these companies to raise capital for research and development. The impact of a recession on the FL market can also depend on the specific industries it serves. For instance, industries like healthcare and finance, which can benefit significantly from privacy-preserving machine learning solutions like Federated Learning, may continue to invest in this technology even during a downturn. While recessions can have short-term effects on technology markets, innovative and disruptive technologies like Federated Learning may still have long-term resilience. Companies looking to build competitive advantages may see FL as a strategic investment that can help them thrive in a post-recession environment. Government policies and initiatives aimed at economic recovery can influence the adoption of Federated Learning. If governments promote digital innovation as part of their recovery strategies, it could create opportunities for FL solutions.
By Component
Solutions
Services
By Application
Data Privacy & Security Management
Risk Management
Shopping Experience Personalization
Industrial Internet of Things
Online Visual Object Detection
Others
By Enterprise Size
Large Enterprises
Small & Medium Enterprises
By Industry Vertical
BFSI
Healthcare & Life Sciences
Retail & E-commerce
Manufacturing
Energy & Utilities
Others
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The industrial Internet of Things, visual object detection, drug discovery, risk management, augmented and virtual reality, data privacy management, and others are some of the applications that make up the global market for federated learning. In 2022, the Industrial Internet of Things (IIoT) category accounted for the biggest revenue share in the global market for federated learning as businesses look to make use of machine learning's advantages to boost productivity and optimize industrial operations. The IIoT refers to the automation and optimization of industrial processes through the use of linked devices, sensors, and other technologies. While protecting the privacy of the training data, federated learning has the potential to enable the development of more precise and effective machine learning models for usage in the IIoT. It is projected that these factors will drive the segment's revenue growth.
Regional Analysis
During the projected period, Europe is anticipated to have the greatest market share in the federated learning industry. Medical imaging and diagnostics, precision medicine, lifestyle management and monitoring, drug discovery, inpatient care and hospital management, virtual assistants, wearables, and research are some of the applications that make up the healthcare sector's federated learning market. The lengthy process of discovering new drugs necessitates the analysis of enormous amounts of bioscience data, such as patents, genomic data, and the numerous papers that are uploaded daily across all biomedical journals and databases. The process of developing drugs must thus advance, and federated learning has the ability to influence and enhance this process. Vendors in the market are therefore creating new items in order to provide a better platform throughout the market. The challenges associated with aging populations and a lack of healthcare personnel in Europe are accelerating the deployment of AI technologies in the healthcare sector. As a result, the market for federated learning in Europe is growing.
Due to the region's growing reliance on mobile technology and the Internet of Things (IoT), particularly in China, Japan, and India, the Asia Pacific market is anticipated to have a notably rapid rate of revenue growth during the projected period. A total of 360.5 million IoT-connected devices were reported by China Telecom in H1 2022, up 21.3% from the 298.2 million connections the company recorded at the end of 2021. The national cellular Internet of Things (IoT) market in China, according to the Ministry of Industry and Information Technology (MIIT), reached 1.67 billion at the end of June 2022, up from 1.42 billion at the start of the year. Because it allows data to be processed locally on these devices rather than being sent to a central server, federated learning is particularly suited to mobile and IoT devices. The user experience can be enhanced and latency reduced as a result. Additionally, it is anticipated that the region's growing use of AI and machine learning (ML) will increase demand for federated learning. In areas like healthcare, finance, and retail where data is frequently sensitive and decentralized, federated learning can be used to train ML models on decentralized data.
North America
US
Canada
Mexico
Europe
Eastern Europe
Poland
Romania
Hungary
Turkey
Rest of Eastern Europe
Western Europe
Germany
France
UK
Italy
Spain
Netherlands
Switzerland
Austria
Rest of Western Europe
Asia Pacific
China
India
Japan
South Korea
Vietnam
Singapore
Australia
Rest of Asia Pacific
Middle East & Africa
Middle East
UAE
Egypt
Saudi Arabia
Qatar
Rest of Middle East
Africa
Nigeria
South Africa
Rest of Africa
Latin America
Brazil
Argentina
Colombia
The major players are Edge Delta Inc., Secure AI Labs, Intellegens Ltd., Decentralized Machine Learning, Microsoft Corporation, Nvidia Corporation, Owkin Inc., Enveil Inc., DataFleets Ltd, International Business Machines Corporation, FEDML, Cloudera Inc, Alphabet Inc., Apheris, Consilient, and others., and others in the final report.
As an innovative end-to-end system for both AI technologies and intelligent healthcare manufacturing and deployment in implantable augmentations, NVIDIA introduced the Communications Intelligence Platform in March 2022. This Clara Holoscan solution, which was created for its healthcare sciences business, has since been updated to MGX.
The OpenVINO toolkit is utilized for online modifications and execution necessary for increased TensorFlow compatibility, and Intel released its integration with TensorFlow in January 2022. It was designed for programmers who want to experiment with the OpenVINO toolset to see if they can improve the performance of current inferential apps with minimal code changes.
NVIDIA improved NVIDIA Clara in June 2021. Homomorphic Encryption (HE), a new tool, was included in NVIDIA Clara Train 4.0. Users can compute encrypted data thanks to it. The communication link is established with Clair Train 4.0 using SSL certificates and a provisioning tool. For example, NVIDIA provided safe aggregating through federated learning by utilizing HE by utilizing the TenSEAL module from OpenMined, a succinct Python wrapper around Microsoft SEAL.Enveil released a new version of ZeroReveal 3.0 in February 2021. Through an effective and decentralized architecture created to lower risk and meet business concerns including data sharing, collaboration, monetization, and regulatory compliance, it gives homomorphic encryption-powered capabilities. With the 3.0 versions, Enveil's ZeroReveal Search and ZeroReveal Machine Learning product lines have stronger integration, performance, and user experience capabilities.
| Report Attributes | Details |
| Market Size in 2022 | US$ 113.2 Mn |
| Market Size by 2030 | US$ 261.8 Mn |
| CAGR | CAGR of 11.05 % From 2023 to 2030 |
| Base Year | 2022 |
| Forecast Period | 2023-2030 |
| Historical Data | 2019-2021 |
| Report Scope & Coverage | Market Size, Segments Analysis, Competitive Landscape, Regional Analysis, DROC & SWOT Analysis, Forecast Outlook |
| Key Segments | • By Component (Solutions, Services) • By Application (Drug Discovery, Data Privacy & Security Management, Risk Management, Shopping Experience Personalization, Industrial Internet of Things, Online Visual Object Detection, Others) • By Enterprise Size (Large Enterprises, Small & Medium Enterprises) • By Industry Vertical (BFSI, Healthcare & Life Sciences, Retail & E-commerce, Manufacturing, Energy & Utilities, Others) |
| Regional Analysis/Coverage | North America (US, Canada, Mexico), Europe (Eastern Europe [Poland, Romania, Hungary, Turkey, Rest of Eastern Europe] Western Europe] Germany, France, UK, Italy, Spain, Netherlands, Switzerland, Austria, Rest of Western Europe]), Asia Pacific (China, India, Japan, South Korea, Vietnam, Singapore, Australia, Rest of Asia Pacific), Middle East & Africa (Middle East [UAE, Egypt, Saudi Arabia, Qatar, Rest of Middle East], Africa [Nigeria, South Africa, Rest of Africa], Latin America (Brazil, Argentina, Colombia Rest of Latin America) |
| Company Profiles | Edge Delta Inc., Secure AI Labs, Intellegens Ltd., Decentralized Machine Learning, Microsoft Corporation, Nvidia Corporation, Owkin Inc., Enveil Inc., DataFleets Ltd, International Business Machines Corporation, FEDML, Cloudera Inc, Alphabet Inc., Apheris, Consilient |
| Key Drivers | • Increasing the use of federated learning for data privacy in numerous areas • To develop the industry, there is a need to improve learning between devices and organizations. |
| Market Restraints | • Insufficient technical expertise |
Ans: The value of the Federated Learning Market is 113.2 billion in 2022.
Ans. The CAGR of the Federated Learning Market is 11.05 %.
ANS: Yes, you can ask for the customization as pas per your business requirement.
Ans. The forecast period of the Federated Learning Market is 2022-2030.
Ans: Four segments are covered in the Federated Learning Market Report, By Component, By Application, By Enterprise Size, By Industry Vertical.
Table of Contents
1. Introduction
1.1 Market Definition
1.2 Scope
1.3 Research Assumptions
2. Research Methodology
3. Market Dynamics
3.1 Drivers
3.2 Restraints
3.3 Opportunities
3.4 Challenges
4. Impact Analysis
4.1 Impact of Russia-Ukraine War
4.2 Impact of Ongoing Recession
4.2.1 Introduction
4.2.2 Impact on major economies
4.2.2.1 US
4.2.2.2 Canada
4.2.2.3 Germany
4.2.2.4 France
4.2.2.5 United Kingdom
4.2.2.6 China
4.2.2.7 japan
4.2.2.8 South Korea
4.2.2.9 Rest of the World
5. Value Chain Analysis
6. Porter’s 5 forces model
7. PEST Analysis
8. Federated Learning Market Segmentation, By Components
8.1 Products
8.2 Services
9. Federated Learning Market Segmentation, By Application
9.1 Small & Medium Enterprises
9.2 Large Enterprises
10. Federated Learning Market Segmentation, By Enterprise Size
10.1 Cloud
10.2 On-premises
11. Federated Learning Market Segmentation, By Industry Vertical
11.1 BFSI
11.2 Retail & Consumer Goods
11.3 Healthcare & Life Science
11.4 Manufacturing
11.5 IT & Telecommunication
11.6 Government & Public Sector
11.7 Others
12. Regional Analysis
12.1 Introduction
12.2 North America
12.2.1 North America Federated Learning Market By Country
12.2.2 North America Federated Learning Market By Components
12.2.3 North America Federated Learning Market By Application
12.2.4 North America Federated Learning Market By Enterprise Size
12.2.5 North America Federated Learning Market By Industry Vertical
12.2.6 USA
12.2.6.1 USA Federated Learning Market By Components
12.2.6.2 USA Federated Learning Market By Application
12.2.6.3 USA Federated Learning Market By Enterprise Size
12.2.6.4 USA Federated Learning Market By Industry Vertical
12.2.7 Canada
12.2.7.1 Canada Federated Learning Market By Components
12.2.7.2 Canada Federated Learning Market By Application
12.2.7.3 Canada Federated Learning Market By Enterprise Size
12.2.7.4 Canada Federated Learning Market By Industry Vertical
12.2.8 Mexico
12.2.8.1 Mexico Federated Learning Market By Components
12.2.8.2 Mexico Federated Learning Market By Application
12.2.8.3 Mexico Federated Learning Market By Enterprise Size
12.2.8.4 Mexico Federated Learning Market By Industry Vertical
12.3 Europe
12.3.1 Eastern Europe
12.3.1.1 Eastern Europe Federated Learning Market By Country
12.3.1.2 Eastern Europe Federated Learning Market By Components
12.3.1.3 Eastern Europe Federated Learning Market By Application
12.3.1.4 Eastern Europe Federated Learning Market By Enterprise Size
12.3.1.5 Eastern Europe Federated Learning Market By Industry Vertical
12.3.1.6 Poland
12.3.1.6.1 Poland Federated Learning Market By Components
12.3.1.6.2 Poland Federated Learning Market By Application
12.3.1.6.3 Poland Federated Learning Market By Enterprise Size
12.3.1.6.4 Poland Federated Learning Market By Industry Vertical
12.3.1.7 Romania
12.3.1.7.1 Romania Federated Learning Market By Components
12.3.1.7.2 Romania Federated Learning Market By Application
12.3.1.7.3 Romania Federated Learning Market By Enterprise Size
12.3.1.7.4 Romania Federated Learning Market By Industry Vertical
12.3.1.8 Hungary
12.3.1.8.1 Hungary Federated Learning Market By Components
12.3.1.8.2 Hungary Federated Learning Market By Application
12.3.1.8.3 Hungary Federated Learning Market By Enterprise Size
12.3.1.8.4 Hungary Federated Learning Market By Industry Vertical
12.3.1.9 Turkey
12.3.1.9.1 Turkey Federated Learning Market By Components
12.3.1.9.2 Turkey Federated Learning Market By Application
12.3.1.9.3 Turkey Federated Learning Market By Enterprise Size
12.3.1.9.4 Turkey Federated Learning Market By Industry Vertical
12.3.1.10 Rest of Eastern Europe
12.3.1.10.1 Rest of Eastern Europe Federated Learning Market By Components
12.3.1.10.2 Rest of Eastern Europe Federated Learning Market By Application
12.3.1.10.3 Rest of Eastern Europe Federated Learning Market By Enterprise Size
12.3.1.10.4 Rest of Eastern Europe Federated Learning Market By Industry Vertical
12.3.2 Western Europe
12.3.2.1 Western Europe Federated Learning Market By Country
12.3.2.2 Western Europe Federated Learning Market By Components
12.3.2.3 Western Europe Federated Learning Market By Application
12.3.2.4 Western Europe Federated Learning Market By Enterprise Size
12.3.2.5 Western Europe Federated Learning Market By Industry Vertical
12.3.2.6 Germany
12.3.2.6.1 Germany Federated Learning Market By Components
12.3.2.6.2 Germany Federated Learning Market By Application
12.3.2.6.3 Germany Federated Learning Market By Enterprise Size
12.3.2.6.4 Germany Federated Learning Market By Industry Vertical
12.3.2.7 France
12.3.2.7.1 France Federated Learning Market By Components
12.3.2.7.2 France Federated Learning Market By Application
12.3.2.7.3 France Federated Learning Market By Enterprise Size
12.3.2.7.4 France Federated Learning Market By Industry Vertical
12.3.2.8 UK
12.3.2.8.1 UK Federated Learning Market By Components
12.3.2.8.2 UK Federated Learning Market By Application
12.3.2.8.3 UK Federated Learning Market By Enterprise Size
12.3.2.8.4 UK Federated Learning Market By Industry Vertical
12.3.2.9 Italy
12.3.2.9.1 Italy Federated Learning Market By Components
12.3.2.9.2 Italy Federated Learning Market By Application
12.3.2.9.3 Italy Federated Learning Market By Enterprise Size
12.3.2.9.4 Italy Federated Learning Market By Industry Vertical
12.3.2.10 Spain
12.3.2.10.1 Spain Federated Learning Market By Components
12.3.2.10.2 Spain Federated Learning Market By Application
12.3.2.10.3 Spain Federated Learning Market By Enterprise Size
12.3.2.10.4 Spain Federated Learning Market By Industry Vertical
12.3.2.11 Netherlands
12.3.2.11.1 Netherlands Federated Learning Market By Components
12.3.2.11.2 Netherlands Federated Learning Market By Application
12.3.2.11.3 Netherlands Federated Learning Market By Enterprise Size
12.3.2.11.4 Netherlands Federated Learning Market By Industry Vertical
12.3.2.12 Switzerland
12.3.2.12.1 Switzerland Federated Learning Market By Components
12.3.2.12.2 Switzerland Federated Learning Market By Application
12.3.2.12.3 Switzerland Federated Learning Market By Enterprise Size
12.3.2.12.4 Switzerland Federated Learning Market By Industry Vertical
12.3.2.13 Austria
12.3.2.13.1 Austria Federated Learning Market By Components
12.3.2.13.2 Austria Federated Learning Market By Application
12.3.2.13.3 Austria Federated Learning Market By Enterprise Size
12.3.2.13.4 Austria Federated Learning Market By Industry Vertical
12.3.2.14 Rest of Western Europe
12.3.2.14.1 Rest of Western Europe Federated Learning Market By Components
12.3.2.14.2 Rest of Western Europe Federated Learning Market By Application
12.3.2.14.3 Rest of Western Europe Federated Learning Market By Enterprise Size
12.3.2.14.4 Rest of Western Europe Federated Learning Market By Industry Vertical
12.4 Asia-Pacific
12.4.1 Asia Pacific Federated Learning Market By Country
12.4.2 Asia Pacific Federated Learning Market By Components
12.4.3 Asia Pacific Federated Learning Market By Application
12.4.4 Asia Pacific Federated Learning Market By Enterprise Size
12.4.5 Asia Pacific Federated Learning Market By Industry Vertical
12.4.6 China
12.4.6.1 China Federated Learning Market By Components
12.4.6.2 China Federated Learning Market By Application
12.4.6.3 China Federated Learning Market By Enterprise Size
12.4.6.4 China Federated Learning Market By Industry Vertical
12.4.7 India
12.4.7.1 India Federated Learning Market By Components
12.4.7.2 India Federated Learning Market By Application
12.4.7.3 India Federated Learning Market By Enterprise Size
12.4.7.4 India Federated Learning Market By Industry Vertical
12.4.8 Japan
12.4.8.1 Japan Federated Learning Market By Components
12.4.8.2 Japan Federated Learning Market By Application
12.4.8.3 Japan Federated Learning Market By Enterprise Size
12.4.8.4 Japan Federated Learning Market By Industry Vertical
12.4.9 South Korea
12.4.9.1 South Korea Federated Learning Market By Components
12.4.9.2 South Korea Federated Learning Market By Application
12.4.9.3 South Korea Federated Learning Market By Enterprise Size
12.4.9.4 South Korea Federated Learning Market By Industry Vertical
12.4.10 Vietnam
12.4.10.1 Vietnam Federated Learning Market By Components
12.4.10.2 Vietnam Federated Learning Market By Application
12.4.10.3 Vietnam Federated Learning Market By Enterprise Size
12.4.10.4 Vietnam Federated Learning Market By Industry Vertical
12.4.11 Singapore
12.4.11.1 Singapore Federated Learning Market By Components
12.4.11.2 Singapore Federated Learning Market By Application
12.4.11.3 Singapore Federated Learning Market By Enterprise Size
12.4.11.4 Singapore Federated Learning Market By Industry Vertical
12.4.12 Australia
12.4.12.1 Australia Federated Learning Market By Components
12.4.12.2 Australia Federated Learning Market By Application
12.4.12.3 Australia Federated Learning Market By Enterprise Size
12.4.12.4 Australia Federated Learning Market By Industry Vertical
12.4.13 Rest of Asia-Pacific
12.4.13.1 Rest of Asia-Pacific Federated Learning Market By Components
12.4.13.2 Rest of Asia-Pacific Federated Learning Market By Application
12.4.13.3 Rest of Asia-Pacific Federated Learning Market By Enterprise Size
12.4.13.4 Rest of Asia-Pacific Federated Learning Market By Industry Vertical
12.5 Middle East & Africa
12.5.1 Middle East
12.5.1.1 Middle East Federated Learning Market By Country
12.5.1.2 Middle East Federated Learning Market By Components
12.5.1.3 Middle East Federated Learning Market By Application
12.5.1.4 Middle East Federated Learning Market By Enterprise Size
12.5.1.5 Middle East Federated Learning Market By Industry Vertical
12.5.1.6 UAE
12.5.1.6.1 UAE Federated Learning Market By Components
12.5.1.6.2 UAE Federated Learning Market By Application
12.5.1.6.3 UAE Federated Learning Market By Enterprise Size
12.5.1.6.4 UAE Federated Learning Market By Industry Vertical
12.5.1.7 Egypt
12.5.1.7.1 Egypt Federated Learning Market By Components
12.5.1.7.2 Egypt Federated Learning Market By Application
12.5.1.7.3 Egypt Federated Learning Market By Enterprise Size
12.5.1.7.4 Egypt Federated Learning Market By Industry Vertical
12.5.1.8 Saudi Arabia
12.5.1.8.1 Saudi Arabia Federated Learning Market By Components
12.5.1.8.2 Saudi Arabia Federated Learning Market By Application
12.5.1.8.3 Saudi Arabia Federated Learning Market By Enterprise Size
12.5.1.8.4 Saudi Arabia Federated Learning Market By Industry Vertical
12.5.1.9 Qatar
12.5.1.9.1 Qatar Federated Learning Market By Components
12.5.1.9.2 Qatar Federated Learning Market By Application
12.5.1.9.3 Qatar Federated Learning Market By Enterprise Size
12.5.1.9.4 Qatar Federated Learning Market By Industry Vertical
12.5.1.10 Rest of Middle East
12.5.1.10.1 Rest of Middle East Federated Learning Market By Components
12.5.1.10.2 Rest of Middle East Federated Learning Market By Application
12.5.1.10.3 Rest of Middle East Federated Learning Market By Enterprise Size
12.5.1.10.4 Rest of Middle East Federated Learning Market By Industry Vertical
12.5.2. Africa
12.5.2.1 Africa Federated Learning Market By Country
12.5.2.2 Africa Federated Learning Market By Components
12.5.2.3 Africa Federated Learning Market By Application
12.5.2.4 Africa Federated Learning Market By Enterprise Size
12.5.2.5 Africa Federated Learning Market By Industry Vertical
12.5.2.6 Nigeria
12.5.2.6.1 Nigeria Federated Learning Market By Components
12.5.2.6.2 Nigeria Federated Learning Market By Application
12.5.2.6.3 Nigeria Federated Learning Market By Enterprise Size
12.5.2.6.4 Nigeria Federated Learning Market By Industry Vertical
12.5.2.7 South Africa
12.5.2.7.1 South Africa Federated Learning Market By Components
12.5.2.7.2 South Africa Federated Learning Market By Application
12.5.2.7.3 South Africa Federated Learning Market By Enterprise Size
12.5.2.7.4 South Africa Federated Learning Market By Industry Vertical
12.5.2.8 Rest of Africa
12.5.2.8.1 Rest of Africa Federated Learning Market By Components
12.5.2.8.2 Rest of Africa Federated Learning Market By Application
12.5.2.8.3 Rest of Africa Federated Learning Market By Enterprise Size
12.5.2.8.4 Rest of Africa Federated Learning Market By Industry Vertical
12.6. Latin America
12.6.1 Latin America Federated Learning Market By Country
12.6.2 Latin America Federated Learning Market By Components
12.6.3 Latin America Federated Learning Market By Application
12.6.4 Latin America Federated Learning Market By Enterprise Size
12.6.5 Latin America Federated Learning Market By Industry Vertical
12.6.6 Brazil
12.6.6.1 Brazil Federated Learning Market By Components
12.6.6.2 Brazil Federated Learning Market By Application
12.6.6.3 Brazil Federated Learning Market By Enterprise Size
12.6.6.4 Brazil Federated Learning Market By Industry Vertical
12.6.7 Argentina
12.6.7.1 Argentina Federated Learning Market By Components
12.6.7.2 Argentina Federated Learning Market By Application
12.6.7.3 Argentina Federated Learning Market By Enterprise Size
12.6.7.4 Argentina Federated Learning Market By Industry Vertical
12.6.8 Colombia
12.6.8.1 Colombia Federated Learning Market By Components
12.6.8.2 Colombia Federated Learning Market By Application
12.6.8.3 Colombia Federated Learning Market By Enterprise Size
12.6.8.4 Colombia Federated Learning Market By Industry Vertical
12.6.9 Rest of Latin America
12.6.9.1 Rest of Latin America Federated Learning Market By Components
12.6.9.2 Rest of Latin America Federated Learning Market By Application
12.6.9.3 Rest of Latin America Federated Learning Market By Enterprise Size
12.6.9.4 Rest of Latin America Federated Learning Market By Industry Vertical
13. Company Profile
13.1 Edge Delta Inc
13.1.1 Company Overview
13.1.2 Financials
13.1.3 Product/Services/Offerings
13.1.4 SWOT Analysis
13.1.5 The SNS View
13.2 Secure AI Labs.
13.2.1 Company Overview
13.2.2 Financials
13.2.3 Product/Services/Offerings
13.2.4 SWOT Analysis
13.2.5 The SNS View
13.3 Intellegens Ltd.
13.3.1 Company Overview
13.3.2 Financials
13.3.3 Product/Services/Offerings
13.3.4 SWOT Analysis
13.3.5 The SNS View
13.4 Decentralized Machine Learning.
13.4.1 Company Overview
13.4.2 Financials
13.4.3 Product/Services/Offerings
13.4.4 SWOT Analysis
13.4.5 The SNS View
13.5 Microsoft Corporation.
13.5.1 Company Overview
13.5.2 Financials
13.5.3 Product/Services/Offerings
13.5.4 SWOT Analysis
13.5.5 The SNS View
13.6 Nvidia Corporation.
13.6.1 Company Overview
13.6.2 Financials
13.6.3 Product/Services/Offerings
13.6.4 SWOT Analysis
13.6.5 The SNS View
13.7 Owkin Inc.
13.7.1 Company Overview
13.7.2 Financials
13.7.3 Product/Services/Offerings
13.7.4 SWOT Analysis
13.7.5 The SNS View
13.8 DataFleets Ltd.
13.8.1 Company Overview
13.8.2 Financials
13.8.3 Product/Services/Offerings
13.8.4 SWOT Analysis
13.8.5 The SNS View
13.9 International Business Machines Corporation.
13.9.1 Company Overview
13.9.2 Financials
13.9.3 Product/Services/Offerings
13.9.4 SWOT Analysis
13.9.5 The SNS View
13.10 Cloudera Inc.
13.10.1 Company Overview
13.10.2 Financials
13.10.3 Product/Services/Offerings
13.10.4 SWOT Analysis
13.10.5 The SNS View
14. Competitive Landscape
14.1 Competitive Benchmarking
14.2 Market Share Analysis
14.3 Recent Developments
14.3.1 Industry News
14.3.2 Company News
14.3 Mergers & Acquisitions
15. USE Cases and Best Practices
16. Conclusion
An accurate research report requires proper strategizing as well as implementation. There are multiple factors involved in the completion of good and accurate research report and selecting the best methodology to compete the research is the toughest part. Since the research reports we provide play a crucial role in any company’s decision-making process, therefore we at SNS Insider always believe that we should choose the best method which gives us results closer to reality. This allows us to reach at a stage wherein we can provide our clients best and accurate investment to output ratio.
Each report that we prepare takes a timeframe of 350-400 business hours for production. Starting from the selection of titles through a couple of in-depth brain storming session to the final QC process before uploading our titles on our website we dedicate around 350 working hours. The titles are selected based on their current market cap and the foreseen CAGR and growth.
The 5 steps process:
Step 1: Secondary Research:
Secondary Research or Desk Research is as the name suggests is a research process wherein, we collect data through the readily available information. In this process we use various paid and unpaid databases which our team has access to and gather data through the same. This includes examining of listed companies’ annual reports, Journals, SEC filling etc. Apart from this our team has access to various associations across the globe across different industries. Lastly, we have exchange relationships with various university as well as individual libraries.
Step 2: Primary Research
When we talk about primary research, it is a type of study in which the researchers collect relevant data samples directly, rather than relying on previously collected data. This type of research is focused on gaining content specific facts that can be sued to solve specific problems. Since the collected data is fresh and first hand therefore it makes the study more accurate and genuine.
We at SNS Insider have divided Primary Research into 2 parts.
Part 1 wherein we interview the KOLs of major players as well as the upcoming ones across various geographic regions. This allows us to have their view over the market scenario and acts as an important tool to come closer to the accurate market numbers. As many as 45 paid and unpaid primary interviews are taken from both the demand and supply side of the industry to make sure we land at an accurate judgement and analysis of the market.
This step involves the triangulation of data wherein our team analyses the interview transcripts, online survey responses and observation of on filed participants. The below mentioned chart should give a better understanding of the part 1 of the primary interview.
Part 2: In this part of primary research the data collected via secondary research and the part 1 of the primary research is validated with the interviews from individual consultants and subject matter experts.
Consultants are those set of people who have at least 12 years of experience and expertise within the industry whereas Subject Matter Experts are those with at least 15 years of experience behind their back within the same space. The data with the help of two main processes i.e., FGDs (Focused Group Discussions) and IDs (Individual Discussions). This gives us a 3rd party nonbiased primary view of the market scenario making it a more dependable one while collation of the data pointers.
Step 3: Data Bank Validation
Once all the information is collected via primary and secondary sources, we run that information for data validation. At our intelligence centre our research heads track a lot of information related to the market which includes the quarterly reports, the daily stock prices, and other relevant information. Our data bank server gets updated every fortnight and that is how the information which we collected using our primary and secondary information is revalidated in real time.
Step 4: QA/QC Process
After all the data collection and validation our team does a final level of quality check and quality assurance to get rid of any unwanted or undesired mistakes. This might include but not limited to getting rid of the any typos, duplication of numbers or missing of any important information. The people involved in this process include technical content writers, research heads and graphics people. Once this process is completed the title gets uploader on our platform for our clients to read it.
Step 5: Final QC/QA Process:
This is the last process and comes when the client has ordered the study. In this process a final QA/QC is done before the study is emailed to the client. Since we believe in giving our clients a good experience of our research studies, therefore, to make sure that we do not lack at our end in any way humanly possible we do a final round of quality check and then dispatch the study to the client.
The Insight Engines Market size was USD 1.4 billion in 2022 and is expected to Reach USD 9.5 billion by 2030 and grow at a CAGR of 27.2 % over the forecast period of 2023-2030.
The 3D Printing Market was valued at USD 16.82 Billion in 2022 and is expected to be worth USD 87.94 Billion by 2030, increasing at a 22.97 % CAGR between 2023 and 2030.
The Data Center Virtualization Market was worth USD 7.96 billion in 2022 and is expected to rise to USD 27.57 billion by 2030, at a CAGR of 16.8% between 2023 and 2030.
The Passenger Information System Market size was valued at USD 26.09 Bn in 2022 and is expected to reach USD 51.56 Bn by 2030, and grow at a CAGR of 8.89 % over the forecast period 2023-2030.
The Cloud Applications Market size was USD 232 billion in 2022 and is expected to Reach USD 755.3 billion by 2030 with a growing CAGR of 15.9 % over the forecast period of 2023-2030.
The Communication Platform-as-a-Service (CPaaS) Market size was valued at USD 10.63 billion in 2022 and is expected to grow to USD 82.02 billion by 2030 and grow at a CAGR of 29.1 % over the forecast period of 2023-2030.
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