Federated Learning Solutions Market
PUBLISHED: 2022 ID: SMRC20778
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Federated Learning Solutions Market

Federated Learning Solutions Market Forecasts to 2028 – Global Analysis By Application (Drug Discovery, Online Visual Object Detection), End User (Energy and Utilities, Healthcare and Life Sciences) and By Geography

4.8 (27 reviews)
4.8 (27 reviews)
Published: 2022 ID: SMRC20778

This report covers the impact of COVID-19 on this global market
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Years Covered

2020-2028

Estimated Year Value (2021)

US $94.28 MN

Projected Year Value (2028)

US $227.36 MN

CAGR (2021 - 2028)

13.4%

Regions Covered

North America, Europe, Asia Pacific, South America, and Middle East & Africa

Countries Covered

US, Canada, Mexico, Germany, UK, Italy, France, Spain, Japan, China, India, Australia, New Zealand, South Korea, Rest of Asia Pacific, South America, Argentina, Brazil, Chile, Middle East & Africa, Saudi Arabia, UAE, Qatar, and South Africa

Largest Market

Asia Pacific

Highest Growing Market

Europe


According to Stratistics MRC, the Global Federated Learning Solutions Market is accounted for $94.28 million in 2021 and is expected to reach $227.36 million by 2028 growing at a CAGR of 13.4% during the forecast period. Federated Learning is a machine learning setting where the objective is to train a high-quality unified model with training data distributed over a large number of clients each with unreliable and relatively slow network connections. Federated learning (also known as collaborative learning) is a machine learning technique that trains an algorithm across multiple decentralized edge devices or servers holding local data samples, without switching them.



Market Dynamics:

Driver:

Ability to ensure better data privacy and security by training algorithms on decentralized devices


Federated learning is being researched by major companies and plays a critical role in supporting privacy-sensitive applications where the training data are distributed at the edge. Federated learning takes a step toward protecting users’ data by sharing model updates. Companies can no longer ignore the growing importance of data privacy and data security. The approach of federated learning has provided a new paradigm for applications leveraging data. Currently, data silos and the focus on data privacy are important challenges for AI, but federated learning could be a solution. It could establish a united model for multiple organizations while the local and sensitive data is protected so that they could benefit together without having to worry about data privacy. Federated learning has received a lot of attention in the way the technology tackles the challenge of protecting users’ privacy by decoupling of data provisioned at end-user equipment and Machine Learning (ML) model aggregation, such as network parameters of deep learning at a centralized server. With federated learning, privacy can be classified in two ways: global privacy and local privacy. Global privacy necessitates that the model updates generated at each round are private to all untrusted third parties other than the central server. At the same time, local privacy further requires that the updates are also private to the server.

Restraint:

Lack of skilled technical expertise


The major issue confronting most organizations while incorporating ML in their business processes is the lack of skilled employees, including IT experts. Since federated learning is a new concept, it becomes difficult for employees to understand and implement federated learning models for training data. This is due to the lack of training provided to employees for implementing federated learning models. Recruiting and retaining technical resources have become a significant focus for several enterprises due to the lack of skilled people to develop and execute federated learning projects that involve complex techniques, such as ML. For example, organizations need engineers who can handle and understand the new federated learning architecture involved with deploying and maintaining ML models.

Opportunity:

Capability to enable predictive features on smart devices


Mobile phones, wearable devices, and autonomous vehicles are just a few of the modern distributed networks generating a wealth of data each day. Owing to the growing computational power of these devices—coupled with concerns related to transmitting private information—it is increasingly attractive to store data locally and push network computation to the edge devices. Federated learning is an emerging approach that helps companies easily collect and store data. Federated learning has the potential to enable predictive features on smartphones without diminishing the user experience or leaking private information. Edge devices, such as smartphones and IoT devices, can benefit from the on-device data without the data ever leaving the device, especially for computationally constrained devices where communication is a bottleneck with smaller devices. Today, industries, such as BFSI, healthcare and life sciences, and retail and eCommerce, collect gigantic amounts of data generated by consumer devices, including mobile phones, tablets, and personal laptops, on a daily basis. The federated learning approach provides a unique way to build such personalized models without intruding users’ privacy.

Threat:

Indirect information leakage


Privacy concerns serve to motivate the desire to keep raw data on each local device in a distributed Machine Learning (ML) setting. However, sharing other information such as model updates as part of the training process brings up another concern—the potential to leak sensitive user information. For instance, it is possible to extract sensitive text patterns, such as a credit card number, from a Recurrent Neural Network (RNN) trained on the user data. Unlike differential privacy protection, the data and the model itself are not transmitted, nor can they be guessed by the other party’s data. Hence, there is a little possibility of leakage at the raw data level. Federated learning exposes intermediate results, such as parameter updates from an optimization algorithm, such as Stochastic Gradient Descent (SGD). However, no security guarantee is provided, and the leakage of these gradients may actually reveal important information when exposed together with data structure, such as in the case of image pixels.

The manufacturing segment is expected to have the highest CAGR during the forecast period

The manufacturing segment is growing at the highest CAGR in the market. Smart manufacturing technologies are extensively accepted by manufacturers to advance the proficiency and efficiency of the industrial process while guaranteeing a high level of safety. In today’s competitive environment growing focus on IIoT with advances in artificial intelligence and machine learning manufacturers can access big data and use learning algorithms to analyze the data. But, the privacy of sensitive data for industries and manufacturing companies is a significant factor. Federated learning algorithms can be useful to these problems as they do not access or reveal any sensitive data.

The healthcare and life sciences segment is expected to be the largest during the forecast period

The healthcare and life sciences segment is expected to be the largest share in the market. The implementation of federated learning solutions by the healthcare sector to predict the disease and its medicine has seen growth in the pandemic situation. The key market players are using these solutions to assist healthcare organizations understand drug effectiveness differences from patient to patient, identifying the best drug used for the right patient at the right time, enhancing the drug development process as well as improving treatment outcomes.

Region with highest share:

Asia Pacific is projected to hold the largest share in the market due to the increasing adoption of advanced technologies in various industries. The demand for federal learning solutions has been increasing with advanced technologies such as AI, IoT, and big data analytics to analyze the collected data. Moreover, emerging industrialization and ongoing development for data regulations in countries like India, China, and Japan are expected to create many lucrative opportunities for the federal learning solutions market.

Region with highest CAGR:

Europe is projected to have the highest CAGR in the market due to the increased adoption of technologies and the presence of a large number of federal learning solution vendors in the region. Other factors like strict data regulations and increasing demand for data privacy is expected to boost the market in Europe.



Key players in the market:

Some of the key players profiled in the Federated Learning Solutions Market include Cloudera, Consilient, DataFleets, Decentralized Machine Learning, Edge Delta, Enveil, Extreme Vision, Google, IBM, Intellegens, Lifebit, Microsoft, NVIDIA, Owkin, and Secure AI Labs.

Key developments:

In March 2021: NVIDIA launched the NVIDIA AI Enterprise, a comprehensive software suite of enterprise-grade AI tools and frameworks optimized, certified, and supported by NVIDIA that run on VMware vSphere. NVIDIA AI Enterprise enables customers to reduce AI model development time from 80 weeks to just eight weeks and allows them to deploy and manage advanced AI applications on VMware vSphere.

In February 2021: Enveil introduced new version of ZeroReveal 3.0. It delivers the homomorphic encryption-powered capabilities through an efficient and decentralized framework designed to reduce risk and address business challenges, including data sharing, collaboration, monetization, and regulatory compliance.

In November 2020: NVIDIA Clara Train 3.1 introduces a flexible authorization framework that enhances security to ensure sensitive data is protected. It also includes a new administration tool that enables a 10x increase in algorithm experimentation to boost researcher productivity. Clara Train 3.1 new features help healthcare developers scale federated learning securely and boost research productivity.

In May 2020: Owkin launched the COVID-19 Open AI Consortium (COAI). The consortium will enable advanced collaborative research and accelerate the clinical development of effective treatments for patients who are infected with COVID-19. In this project, Owkin used federated learning, aiming to help healthcare companies understand why drug efficacy varies from patient-to-patient, enhance the drug development process, and identify the best drug for the right patient at the right time, to improve treatment outcomes.

Applications Covered:
• Drug Discovery      
• Industrial Internet of Things (IIoT)      
• Online Visual Object Detection       
• Risk Management      
• Shopping Experience Personalization      
• Data Privacy and Security Management      
• Other Applications       

End Users Covered:
• Banking, Financial Services and Insurance (BFSI)      
• Energy and Utilities      
• Healthcare and Life Sciences      
• Manufacturing      
• Retail and E-Commerce      
• Other End Users       

Regions Covered:
• North America
o US
o Canada
o Mexico
• Europe
o Germany
o UK
o Italy
o France
o Spain
o Rest of Europe
• Asia Pacific
o Japan       
o China       
o India       
o Australia 
o New Zealand
o South Korea
o Rest of Asia Pacific   
• South America
o Argentina
o Brazil
o Chile
o Rest of South America
• Middle East & Africa
o Saudi Arabia
o UAE
o Qatar
o South Africa
o Rest of Middle East & Africa

What our report offers:
- Market share assessments for the regional and country-level segments
- Strategic recommendations for the new entrants
- Covers Market data for the years 2020, 2021, 2022, 2025, and 2028
- Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
- Strategic recommendations in key business segments based on the market estimations
- Competitive landscaping mapping the key common trends
- Company profiling with detailed strategies, financials, and recent developments
- Supply chain trends mapping the latest technological advancements

Free Customization Offerings:
All the customers of this report will be entitled to receive one of the following free customization options:
• Company Profiling
o Comprehensive profiling of additional market players (up to 3)
o SWOT Analysis of key players (up to 3)
• Regional Segmentation
o Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
• Competitive Benchmarking
o Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Key Questions Answered In The Report

The Global Federated Learning Solutions Market is majorly driven by the ability to ensure better data privacy and security by training algorithms on decentralized devices.

The manufacturing segment is growing at a highest CAGR owing to smart manufacturing technologies are extensively accepted by manufacturers to advance the proficiency and efficiency of the industrial process while guaranteeing a high level of safety.

North America dominated with a significant market share due to the factors such as stringent data regulations and high focus on data privacy, focus on innovation through research, expected to propel the growth across the region.

Europe market is growing at a highest CAGR owing to the growing acceptance of technologies and the existence of a large number of federal learning solution vendors in the region.

Key players in Federated Learning Solutions Market include Cloudera, Consilient, DataFleets, Decentralized Machine Learning, Edge Delta, Enveil, Extreme Vision, Google, IBM, Intellegens, Lifebit, Microsoft, NVIDIA, Owkinand Secure AI Labs Company.

Table of Contents

1 Executive Summary   
    
2 Preface   

 2.1 Abstract  
 2.2 Stake Holders  
 2.3 Research Scope  
 2.4 Research Methodology  
  2.4.1 Data Mining 
  2.4.2 Data Analysis 
  2.4.3 Data Validation 
  2.4.4 Research Approach 
 2.5 Research Sources  
  2.5.1 Primary Research Sources 
  2.5.2 Secondary Research Sources 
  2.5.3 Assumptions 
    
3 Market Trend Analysis   
 3.1 Introduction  
 3.2 Drivers  
 3.3 Restraints  
 3.4 Opportunities  
 3.5 Threats  
 3.6 Application Analysis  
 3.7 End User Analysis  
 3.8 Emerging Markets  
 3.9 Impact of Covid-19  
    
4 Porters Five Force Analysis   
 4.1 Bargaining power of suppliers  
 4.2 Bargaining power of buyers  
 4.3 Threat of substitutes  
 4.4 Threat of new entrants  
 4.5 Competitive rivalry  
    
5 Global Federated Learning Solutions Market, By Application   
 5.1 Introduction  
 5.2 Drug Discovery         
 5.3 Industrial Internet of Things (IIoT)         
 5.4 Online Visual Object Detection         
 5.5 Risk Management         
 5.6 Shopping Experience Personalization          
 5.7 Data Privacy and Security Management         
 5.8 Other Applications          
  5.8.1 Anomaly Detection 
  5.8.2 Corporate Information Technology (IT)     
  5.8.3 Genomics       
  5.8.4 Video Analytics       
    
6 Global Federated Learning Solutions Market, By End User   
 6.1 Introduction  
 6.2 Banking, Financial Services and Insurance (BFSI)         
 6.3 Energy and Utilities         
 6.4 Healthcare and Life Sciences         
 6.5 Manufacturing         
 6.6 Retail and E-Commerce         
 6.7 Other End Users          
  6.7.1 Government 
  6.7.2 Media and Entertainment        
  6.7.3 Telecommunications and Information Technology (IT)       
    
7 Global Federated Learning Solutions Market, By Geography   
 7.1 Introduction  
 7.2 North America  
  7.2.1 US 
  7.2.2 Canada 
  7.2.3 Mexico 
 7.3 Europe  
  7.3.1 Germany 
  7.3.2 UK 
  7.3.3 Italy 
  7.3.4 France 
  7.3.5 Spain 
  7.3.6 Rest of Europe 
 7.4 Asia Pacific  
  7.4.1 Japan 
  7.4.2 China 
  7.4.3 India 
  7.4.4 Australia 
  7.4.5 New Zealand 
  7.4.6 South Korea 
  7.4.7 Rest of Asia Pacific 
 7.5 South America  
  7.5.1 Argentina 
  7.5.2 Brazil 
  7.5.3 Chile 
  7.5.4 Rest of South America 
 7.6 Middle East & Africa  
  7.6.1 Saudi Arabia 
  7.6.2 UAE 
  7.6.3 Qatar 
  7.6.4 South Africa 
  7.6.5 Rest of Middle East & Africa 
    
8 Key Developments   
 8.1 Agreements, Partnerships, Collaborations and Joint Ventures  
 8.2 Acquisitions & Mergers  
 8.3 New Product Launch  
 8.4 Expansions  
 8.5 Other Key Strategies  
    
9 Company Profiling   
 9.1 Cloudera         
 9.2 Consilient         
 9.3 DataFleets         
 9.4 Decentralized Machine Learning         
 9.5 Edge Delta         
 9.6 Enveil         
 9.7 Extreme Vision         
 9.8 Google         
 9.9 IBM         
 9.10 Intellegens         
 9.11 Lifebit         
 9.12 Microsoft         
 9.13 NVIDIA         
 9.14 Owkin         
 9.15 Secure AI Labs  


List of Tables    
1 Global Federated Learning Solutions Market Outlook, By Region (2020-2028) ($MN)   
2 Global Federated Learning Solutions Market Outlook, By Application (2020-2028) ($MN)   
3 Global Federated Learning Solutions Market Outlook, By Drug Discovery (2020-2028) ($MN)   
4 Global Federated Learning Solutions Market Outlook, By Industrial Internet of Things (IIoT) (2020-2028) ($MN)   
5 Global Federated Learning Solutions Market Outlook, By Online Visual Object Detection (2020-2028) ($MN)   
6 Global Federated Learning Solutions Market Outlook, By Risk Management (2020-2028) ($MN)   
7 Global Federated Learning Solutions Market Outlook, By Shopping Experience Personalization (2020-2028) ($MN)   
8 Global Federated Learning Solutions Market Outlook, By Data Privacy and Security Management (2020-2028) ($MN)   
9 Global Federated Learning Solutions Market Outlook, By Other Applications (2020-2028) ($MN)   
10 Global Federated Learning Solutions Market Outlook, By Anomaly Detection (2020-2028) ($MN)   
11 Global Federated Learning Solutions Market Outlook, By Corporate Information Technology (IT)    (2020-2028) ($MN)   
12 Global Federated Learning Solutions Market Outlook, By Genomics (2020-2028) ($MN)   
13 Global Federated Learning Solutions Market Outlook, By Video Analytics (2020-2028) ($MN)   
14 Global Federated Learning Solutions Market Outlook, By End User (2020-2028) ($MN)   
15 Global Federated Learning Solutions Market Outlook, By Banking, Financial Services and Insurance (BFSI) (2020-2028) ($MN)   
16 Global Federated Learning Solutions Market Outlook, By Energy and Utilities (2020-2028) ($MN)   
17 Global Federated Learning Solutions Market Outlook, By Healthcare and Life Sciences (2020-2028) ($MN)   
18 Global Federated Learning Solutions Market Outlook, By Manufacturing (2020-2028) ($MN)   
19 Global Federated Learning Solutions Market Outlook, By Retail and E-Commerce (2020-2028) ($MN)   
20 Global Federated Learning Solutions Market Outlook, By Other End Users (2020-2028) ($MN)   
21 Global Federated Learning Solutions Market Outlook, By Government (2020-2028) ($MN)   
22 Global Federated Learning Solutions Market Outlook, By Media and Entertainment (2020-2028) ($MN)   
23 Global Federated Learning Solutions Market Outlook, By Telecommunications and Information Technology (IT) (2020-2028) ($MN)   
24 North America Federated Learning Solutions Market Outlook, By Country (2020-2028) ($MN)   
25 North America Federated Learning Solutions Market Outlook, By Application (2020-2028) ($MN)   
26 North America Federated Learning Solutions Market Outlook, By Drug Discovery (2020-2028) ($MN)   
27 North America Federated Learning Solutions Market Outlook, By Industrial Internet of Things (IIoT) (2020-2028) ($MN)   
28 North America Federated Learning Solutions Market Outlook, By Online Visual Object Detection (2020-2028) ($MN)   
29 North America Federated Learning Solutions Market Outlook, By Risk Management (2020-2028) ($MN)   
30 North America Federated Learning Solutions Market Outlook, By Shopping Experience Personalization (2020-2028) ($MN)   
31 North America Federated Learning Solutions Market Outlook, By Data Privacy and Security Management (2020-2028) ($MN)   
32 North America Federated Learning Solutions Market Outlook, By Other Applications (2020-2028) ($MN)   
33 North America Federated Learning Solutions Market Outlook, By Anomaly Detection (2020-2028) ($MN)   
34 North America Federated Learning Solutions Market Outlook, By Corporate Information Technology (IT)    (2020-2028) ($MN)   
35 North America Federated Learning Solutions Market Outlook, By Genomics (2020-2028) ($MN)   
36 North America Federated Learning Solutions Market Outlook, By Video Analytics (2020-2028) ($MN)   
37 North America Federated Learning Solutions Market Outlook, By End User (2020-2028) ($MN)   
38 North America Federated Learning Solutions Market Outlook, By Banking, Financial Services and Insurance (BFSI) (2020-2028) ($MN)   
39 North America Federated Learning Solutions Market Outlook, By Energy and Utilities (2020-2028) ($MN)   
40 North America Federated Learning Solutions Market Outlook, By Healthcare and Life Sciences (2020-2028) ($MN)   
41 North America Federated Learning Solutions Market Outlook, By Manufacturing (2020-2028) ($MN)   
42 North America Federated Learning Solutions Market Outlook, By Retail and E-Commerce (2020-2028) ($MN)   
43 North America Federated Learning Solutions Market Outlook, By Other End Users (2020-2028) ($MN)   
44 North America Federated Learning Solutions Market Outlook, By Government (2020-2028) ($MN)   
45 North America Federated Learning Solutions Market Outlook, By Media and Entertainment (2020-2028) ($MN)   
46 North America Federated Learning Solutions Market Outlook, By Telecommunications and Information Technology (IT) (2020-2028) ($MN)   
47 Europe Federated Learning Solutions Market Outlook, By Country (2020-2028) ($MN)   
48 Europe Federated Learning Solutions Market Outlook, By Application (2020-2028) ($MN)   
49 Europe Federated Learning Solutions Market Outlook, By Drug Discovery (2020-2028) ($MN)   
50 Europe Federated Learning Solutions Market Outlook, By Industrial Internet of Things (IIoT) (2020-2028) ($MN)   
51 Europe Federated Learning Solutions Market Outlook, By Online Visual Object Detection (2020-2028) ($MN)   
52 Europe Federated Learning Solutions Market Outlook, By Risk Management (2020-2028) ($MN)   
53 Europe Federated Learning Solutions Market Outlook, By Shopping Experience Personalization (2020-2028) ($MN)   
54 Europe Federated Learning Solutions Market Outlook, By Data Privacy and Security Management (2020-2028) ($MN)   
55 Europe Federated Learning Solutions Market Outlook, By Other Applications (2020-2028) ($MN)   
56 Europe Federated Learning Solutions Market Outlook, By Anomaly Detection (2020-2028) ($MN)   
57 Europe Federated Learning Solutions Market Outlook, By Corporate Information Technology (IT)    (2020-2028) ($MN)   
58 Europe Federated Learning Solutions Market Outlook, By Genomics (2020-2028) ($MN)   
59 Europe Federated Learning Solutions Market Outlook, By Video Analytics (2020-2028) ($MN)   
60 Europe Federated Learning Solutions Market Outlook, By End User (2020-2028) ($MN)   
61 Europe Federated Learning Solutions Market Outlook, By Banking, Financial Services and Insurance (BFSI) (2020-2028) ($MN)   
62 Europe Federated Learning Solutions Market Outlook, By Energy and Utilities (2020-2028) ($MN)   
63 Europe Federated Learning Solutions Market Outlook, By Healthcare and Life Sciences (2020-2028) ($MN)   
64 Europe Federated Learning Solutions Market Outlook, By Manufacturing (2020-2028) ($MN)   
65 Europe Federated Learning Solutions Market Outlook, By Retail and E-Commerce (2020-2028) ($MN)   
66 Europe Federated Learning Solutions Market Outlook, By Other End Users (2020-2028) ($MN)   
67 Europe Federated Learning Solutions Market Outlook, By Government (2020-2028) ($MN)   
68 Europe Federated Learning Solutions Market Outlook, By Media and Entertainment (2020-2028) ($MN)   
69 Europe Federated Learning Solutions Market Outlook, By Telecommunications and Information Technology (IT) (2020-2028) ($MN)   
70 Asia Pacific Federated Learning Solutions Market Outlook, By Country (2020-2028) ($MN)   
71 Asia Pacific Federated Learning Solutions Market Outlook, By Application (2020-2028) ($MN)   
72 Asia Pacific Federated Learning Solutions Market Outlook, By Drug Discovery (2020-2028) ($MN)   
73 Asia Pacific Federated Learning Solutions Market Outlook, By Industrial Internet of Things (IIoT) (2020-2028) ($MN)   
74 Asia Pacific Federated Learning Solutions Market Outlook, By Online Visual Object Detection (2020-2028) ($MN)   
75 Asia Pacific Federated Learning Solutions Market Outlook, By Risk Management (2020-2028) ($MN)   
76 Asia Pacific Federated Learning Solutions Market Outlook, By Shopping Experience Personalization (2020-2028) ($MN)   
77 Asia Pacific Federated Learning Solutions Market Outlook, By Data Privacy and Security Management (2020-2028) ($MN)   
78 Asia Pacific Federated Learning Solutions Market Outlook, By Other Applications (2020-2028) ($MN)   
79 Asia Pacific Federated Learning Solutions Market Outlook, By Anomaly Detection (2020-2028) ($MN)   
80 Asia Pacific Federated Learning Solutions Market Outlook, By Corporate Information Technology (IT)    (2020-2028) ($MN)   
81 Asia Pacific Federated Learning Solutions Market Outlook, By Genomics (2020-2028) ($MN)   
82 Asia Pacific Federated Learning Solutions Market Outlook, By Video Analytics (2020-2028) ($MN)   
83 Asia Pacific Federated Learning Solutions Market Outlook, By End User (2020-2028) ($MN)   
84 Asia Pacific Federated Learning Solutions Market Outlook, By Banking, Financial Services and Insurance (BFSI) (2020-2028) ($MN)   
85 Asia Pacific Federated Learning Solutions Market Outlook, By Energy and Utilities (2020-2028) ($MN)   
86 Asia Pacific Federated Learning Solutions Market Outlook, By Healthcare and Life Sciences (2020-2028) ($MN)   
87 Asia Pacific Federated Learning Solutions Market Outlook, By Manufacturing (2020-2028) ($MN)   
88 Asia Pacific Federated Learning Solutions Market Outlook, By Retail and E-Commerce (2020-2028) ($MN)   
89 Asia Pacific Federated Learning Solutions Market Outlook, By Other End Users (2020-2028) ($MN)   
90 Asia Pacific Federated Learning Solutions Market Outlook, By Government (2020-2028) ($MN)   
91 Asia Pacific Federated Learning Solutions Market Outlook, By Media and Entertainment (2020-2028) ($MN)   
92 Asia Pacific Federated Learning Solutions Market Outlook, By Telecommunications and Information Technology (IT) (2020-2028) ($MN)   
93 South America Federated Learning Solutions Market Outlook, By Country (2020-2028) ($MN)   
94 South America Federated Learning Solutions Market Outlook, By Application (2020-2028) ($MN)   
95 South America Federated Learning Solutions Market Outlook, By Drug Discovery (2020-2028) ($MN)   
96 South America Federated Learning Solutions Market Outlook, By Industrial Internet of Things (IIoT) (2020-2028) ($MN)   
97 South America Federated Learning Solutions Market Outlook, By Online Visual Object Detection (2020-2028) ($MN)   
98 South America Federated Learning Solutions Market Outlook, By Risk Management (2020-2028) ($MN)   
99 South America Federated Learning Solutions Market Outlook, By Shopping Experience Personalization (2020-2028) ($MN)   
100 South America Federated Learning Solutions Market Outlook, By Data Privacy and Security Management (2020-2028) ($MN)   
101 South America Federated Learning Solutions Market Outlook, By Other Applications (2020-2028) ($MN)   
102 South America Federated Learning Solutions Market Outlook, By Anomaly Detection (2020-2028) ($MN)   
103 South America Federated Learning Solutions Market Outlook, By Corporate Information Technology (IT)    (2020-2028) ($MN)   
104 South America Federated Learning Solutions Market Outlook, By Genomics (2020-2028) ($MN)   
105 South America Federated Learning Solutions Market Outlook, By Video Analytics (2020-2028) ($MN)   
106 South America Federated Learning Solutions Market Outlook, By End User (2020-2028) ($MN)   
107 South America Federated Learning Solutions Market Outlook, By Banking, Financial Services and Insurance (BFSI) (2020-2028) ($MN)   
108 South America Federated Learning Solutions Market Outlook, By Energy and Utilities (2020-2028) ($MN)   
109 South America Federated Learning Solutions Market Outlook, By Healthcare and Life Sciences (2020-2028) ($MN)   
110 South America Federated Learning Solutions Market Outlook, By Manufacturing (2020-2028) ($MN)   
111 South America Federated Learning Solutions Market Outlook, By Retail and E-Commerce (2020-2028) ($MN)   
112 South America Federated Learning Solutions Market Outlook, By Other End Users (2020-2028) ($MN)   
113 South America Federated Learning Solutions Market Outlook, By Government (2020-2028) ($MN)   
114 South America Federated Learning Solutions Market Outlook, By Media and Entertainment (2020-2028) ($MN)   
115 South America Federated Learning Solutions Market Outlook, By Telecommunications and Information Technology (IT) (2020-2028) ($MN)   
116 Middle East & Africa Federated Learning Solutions Market Outlook, By Country (2020-2028) ($MN)   
117 Middle East & Africa Federated Learning Solutions Market Outlook, By Application (2020-2028) ($MN)   
118 Middle East & Africa Federated Learning Solutions Market Outlook, By Drug Discovery (2020-2028) ($MN)   
119 Middle East & Africa Federated Learning Solutions Market Outlook, By Industrial Internet of Things (IIoT) (2020-2028) ($MN)   
120 Middle East & Africa Federated Learning Solutions Market Outlook, By Online Visual Object Detection (2020-2028) ($MN)   
121 Middle East & Africa Federated Learning Solutions Market Outlook, By Risk Management (2020-2028) ($MN)   
122 Middle East & Africa Federated Learning Solutions Market Outlook, By Shopping Experience Personalization (2020-2028) ($MN)   
123 Middle East & Africa Federated Learning Solutions Market Outlook, By Data Privacy and Security Management (2020-2028) ($MN)   
124 Middle East & Africa Federated Learning Solutions Market Outlook, By Other Applications (2020-2028) ($MN)   
125 Middle East & Africa Federated Learning Solutions Market Outlook, By Anomaly Detection (2020-2028) ($MN)   
126 Middle East & Africa Federated Learning Solutions Market Outlook, By Corporate Information Technology (IT) (2020-2028) ($MN)   
127 Middle East & Africa Federated Learning Solutions Market Outlook, By Genomics (2020-2028) ($MN)   
128 Middle East & Africa Federated Learning Solutions Market Outlook, By Video Analytics (2020-2028) ($MN)   
129 Middle East & Africa Federated Learning Solutions Market Outlook, By End User (2020-2028) ($MN)   
130 Middle East & Africa Federated Learning Solutions Market Outlook, By Banking, Financial Services and Insurance (BFSI) (2020-2028) ($MN)   
131 Middle East & Africa Federated Learning Solutions Market Outlook, By Energy and Utilities (2020-2028) ($MN)   
132 Middle East & Africa Federated Learning Solutions Market Outlook, By Healthcare and Life Sciences (2020-2028) ($MN)   
133 Middle East & Africa Federated Learning Solutions Market Outlook, By Manufacturing (2020-2028) ($MN)   
134 Middle East & Africa Federated Learning Solutions Market Outlook, By Retail and E-Commerce (2020-2028) ($MN)   
135 Middle East & Africa Federated Learning Solutions Market Outlook, By Other End Users (2020-2028) ($MN)   
136 Middle East & Africa Federated Learning Solutions Market Outlook, By Government (2020-2028) ($MN)   
137 Middle East & Africa Federated Learning Solutions Market Outlook, By Media and Entertainment (2020-2028) ($MN)   
138 Middle East & Africa Federated Learning Solutions Market Outlook, By Telecommunications and Information Technology (IT) (2020-2028) ($MN)   

List of Figures

RESEARCH METHODOLOGY


Research Methodology

We at Stratistics opt for an extensive research approach which involves data mining, data validation, and data analysis. The various research sources include in-house repository, secondary research, competitor’s sources, social media research, client internal data, and primary research.

Our team of analysts prefers the most reliable and authenticated data sources in order to perform the comprehensive literature search. With access to most of the authenticated data bases our team highly considers the best mix of information through various sources to obtain extensive and accurate analysis.

Each report takes an average time of a month and a team of 4 industry analysts. The time may vary depending on the scope and data availability of the desired market report. The various parameters used in the market assessment are standardized in order to enhance the data accuracy.

Data Mining

The data is collected from several authenticated, reliable, paid and unpaid sources and is filtered depending on the scope & objective of the research. Our reports repository acts as an added advantage in this procedure. Data gathering from the raw material suppliers, distributors and the manufacturers is performed on a regular basis, this helps in the comprehensive understanding of the products value chain. Apart from the above mentioned sources the data is also collected from the industry consultants to ensure the objective of the study is in the right direction.

Market trends such as technological advancements, regulatory affairs, market dynamics (Drivers, Restraints, Opportunities and Challenges) are obtained from scientific journals, market related national & international associations and organizations.

Data Analysis

From the data that is collected depending on the scope & objective of the research the data is subjected for the analysis. The critical steps that we follow for the data analysis include:

  • Product Lifecycle Analysis
  • Competitor analysis
  • Risk analysis
  • Porters Analysis
  • PESTEL Analysis
  • SWOT Analysis

The data engineering is performed by the core industry experts considering both the Marketing Mix Modeling and the Demand Forecasting. The marketing mix modeling makes use of multiple-regression techniques to predict the optimal mix of marketing variables. Regression factor is based on a number of variables and how they relate to an outcome such as sales or profits.


Data Validation

The data validation is performed by the exhaustive primary research from the expert interviews. This includes telephonic interviews, focus groups, face to face interviews, and questionnaires to validate our research from all aspects. The industry experts we approach come from the leading firms, involved in the supply chain ranging from the suppliers, distributors to the manufacturers and consumers so as to ensure an unbiased analysis.

We are in touch with more than 15,000 industry experts with the right mix of consultants, CEO's, presidents, vice presidents, managers, experts from both supply side and demand side, executives and so on.

The data validation involves the primary research from the industry experts belonging to:

  • Leading Companies
  • Suppliers & Distributors
  • Manufacturers
  • Consumers
  • Industry/Strategic Consultants

Apart from the data validation the primary research also helps in performing the fill gap research, i.e. providing solutions for the unmet needs of the research which helps in enhancing the reports quality.


For more details about research methodology, kindly write to us at info@strategymrc.com

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