Federated Learning Solutions Market
PUBLISHED: 2024 ID: SMRC26485
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Federated Learning Solutions Market

Federated Learning Solutions Market Forecasts to 2030 - Global Analysis By Deployment Model (Cloud-based, On-premises, Hybrid and Other Deployment Models), Organization Size (Small and Medium-sized Enterprises (SMEs) and Large Enterprises), Application, End User and By Geography

4.9 (47 reviews)
4.9 (47 reviews)
Published: 2024 ID: SMRC26485

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

2022-2030

Estimated Year Value (2024)

US $137.49 MN

Projected Year Value (2030)

US $292.37 MN

CAGR (2024 - 2030)

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

North America

Highest Growing Market

Asia Pacific


According to Stratistics MRC, the Global Federated Learning Solutions Market is accounted for $137.49 million in 2024 and is expected to reach $292.37 million by 2030 growing at a CAGR of 13.4% during the forecast period. Federated learning solutions, which provide a means of training models cooperatively across decentralized devices or servers while guaranteeing data privacy and security, represent a paradigm shift in the field of machine learning. Federated learning sends models to the data locations, where local training takes place, as an alternative to combining raw data from various sources into a single server. The underlying data is never shared; instead, the locally trained models are combined to produce a global model. Moreover, this strategy is especially helpful in industries like healthcare, finance, and telecommunications, where security concerns and privacy laws make it difficult to share sensitive data.

According to the World Health Organization (WHO), addressing social determinants of health is crucial for improving health equity and outcomes across populations.

Market Dynamics: 

Driver: 

Increasing use of iot devices

The number of connected devices has increased exponentially as a result of the Internet of Things (IoT), producing massive amounts of data at the network's edge. These gadgets, which range from industrial sensors to smart home appliances, generate useful data that can be utilized to gain new perspectives and boost productivity. Without taxing network capacity, federated learning provides a scalable way to use this data for machine learning. Additionally, federated learning enables real-time analytics and decision-making at the edge by reducing the need for central storage and large-scale data transmission by processing data locally on IoT devices.

Restraint:

Exorbitant costs of computation and communication

Federated learning is expensive to communicate with and requires a lot of processing power. Local training is required for every participating device, and it can be resource-intensive, particularly for complex models. These specifications may be difficult for devices with low processing power, like outdated smartphones or IoT sensors, which could result in inconsistent performance and possible delays. Furthermore, in large-scale deployments with thousands or millions of devices, frequent communication between the devices and the central server to aggregate model updates can consume a large amount of bandwidth.

Opportunity:

Growth in privacy-concerned sectors

Federated learning presents a great deal of potential for sectors like healthcare, finance, and law, where data security and privacy are critical concerns. By utilizing data from several clinics and hospitals while protecting patient privacy, federated learning in healthcare can facilitate the creation of predictive models for illness detection and patient care. Moreover, in the financial sector, it can improve credit scoring and fraud detection by leveraging private financial data from multiple institutions. While preserving client confidentiality, legal firms can use federated learning to examine delicate legal documents and case histories.

Threat:

Risks to privacy and security

Federated learning is intended to improve data privacy, but security risks still exist. A variety of attacks, including membership inference and model inversion, can be launched by adversaries to obtain private data from the shared model updates. Malicious participants may also introduce tainted data or faulty model updates into the training process, which could result in compromised results or worse model performance. Additionally, it's important but difficult to create and deploy strong defenses like secure aggregation, anomaly detection, and differential privacy.

Covid-19 Impact: 

The COVID-19 pandemic has expedited the implementation of federated learning solutions, as institutions from diverse sectors aim to utilize data for crucial insights while upholding strict standards for data privacy and security. The necessity for decentralized data processing technologies was brought to light by the trend toward remote work and the growing reliance on digital infrastructure. Furthermore, federated learning has attracted a lot of interest in the healthcare industry because of the pressing need to create predictive models for patient outcomes and virus spread without breaking privacy laws.

The Cloud-based segment is expected to be the largest during the forecast period

In the market for federated learning solutions, the cloud-based segment commands the largest share. Solutions for cloud-based federated learning have several benefits in terms of cost-effectiveness, scalability, and flexibility. By utilizing the extensive processing power and storage capacity of cloud infrastructure, these solutions help enterprises effectively handle and process massive federated learning assignments. Moreover, the cloud is a perfect environment for federated learning because of its built-in capacity to enable smooth collaboration and data sharing across dispersed networks, especially for businesses with multiple locations.

The Small and Medium-sized Enterprises (SMEs) segment is expected to have the highest CAGR during the forecast period

The Small and Medium-sized Enterprises (SMEs) segment of the Federated Learning Solutions Market is anticipated to grow at the highest CAGR. Due to the increasing demand for data-driven insights without sacrificing security and privacy, SMEs are adopting federated learning solutions at a rate that is increasing. SMEs frequently lack the substantial infrastructure and resources needed for conventional centralized data processing, in contrast to large corporations. Federated learning offers SMEs an affordable and expandable substitute that lets them leverage the potential of machine learning on decentralized data.

Region with largest share:

North America holds the largest market share in the Federated Learning Solutions market. The strong presence of important market players, technological developments, and the rapid adoption of cutting-edge technologies across a wide range of industries are all credited with this dominance. Federated learning solutions are growing due to North America's robust IT infrastructure, favorable regulatory environment, and large investments in research and development. Moreover, the adoption of federated learning in industries like healthcare, finance, retail, and telecommunications is fueled by the region's emphasis on data privacy and security, as well as the rising demand for personalized services and predictive analytics.

Region with highest CAGR:

The market for Federated Learning Solutions is anticipated to grow at the highest CAGR in Asia-Pacific. Rapid digital transformation, growing cloud-based technology adoption, and rising investments in AI and machine learning across a range of industry verticals are some of the factors driving this growth. Significant progress in data analytics, IoT, and edge computing is being made in countries like China, India, Japan, and South Korea, which is increasing demand for privacy-preserving machine learning solutions like federated learning. Additionally, the growing awareness of data privacy and security concerns, along with government initiatives to encourage innovation and digitalization, all contribute to the expanding market opportunities in the Asia-Pacific region.

Key players in the market

Some of the key players in Federated Learning Solutions market include Microsoft Corporation, DataFleets Ltd, IBM Corporation, Alphabet Inc, Nvidia Corporation, Enveil Inc, Owkin Inc., Edge Delta Inc, Intellegens Ltd, Secure AI Labs, Cloudera Inc and Sherpa.ai.

Key Developments:

In June 2024, Multinational technology company IBM and Rapidus Corporation, a manufacturer of advanced logic semiconductors, announced a joint development partnership aimed at establishing mass production technologies for chiplet packages. Through this agreement, Rapidus will receive packaging technology from IBM for high-performance semiconductors, and the two companies will collaborate with the aim to further innovate in this space.

In May 2024, Microsoft Corp and Brookfield Asset Management's renewable energy arm has signed a record-breaking clean energy agreement, according to a statement released Wednesday. The partnership comes as Microsoft ramps up its investment in artificial intelligence, Bloomberg reported. Tech companies are increasingly seeking clean energy solutions to meet their own sustainability goals while grappling with rising overall energy demands. 

In February 2024, Google announced a series of Power Purchase Agreements (PPAs) across Europe for more than 700 MW of clean energy, enabling the company to reach more than 90% carbon-free energy in areas including the Netherlands, Italy and Poland, and close to 85% in Belgium in the next two years.

Deployment Models Covered:
• Cloud-based
• On-premises
• Hybrid
• Other Deployment Models 
 
Organization Sizes Covered:
• Small and Medium-sized Enterprises (SMEs)
• Large Enterprises 

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

End Users Covered:
• BFSI
• Healthcare and Life Sciences
• Retail and E-commerce
• Manufacturing
• Energy and Utilities
• Government
• Media and Entertainment
• Telecommunications and Information Technology (IT)
• 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 2022, 2023, 2024, 2026, and 2030
- 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
Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

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 Deployment Model       
 5.1 Introduction      
 5.2 Cloud-based      
 5.3 On-premises      
 5.4 Hybrid      
 5.5 Other Deployment Models      
        
6 Global Federated Learning Solutions Market, By Organization Size       
 6.1 Introduction      
 6.2 Small and Medium-sized Enterprises (SMEs)      
 6.3 Large Enterprises      
        
7 Global Federated Learning Solutions Market, By Application       
 7.1 Introduction      
 7.2 Drug Discovery      
 7.3 Data Privacy & Security Management      
 7.4 Risk Management      
 7.5 Shopping Experience Personalization      
 7.6 Industrial Internet of Things      
 7.7 Online Visual Object Detection      
 7.8 Other Applications      
        
8 Global Federated Learning Solutions Market, By End User       
 8.1 Introduction      
 8.2 BFSI      
 8.3 Healthcare and Life Sciences      
 8.4 Retail and E-commerce      
 8.5 Manufacturing      
 8.6 Energy and Utilities      
 8.7 Government      
 8.8 Media and Entertainment      
 8.9 Telecommunications and Information Technology (IT)      
 8.10 Other End Users      
        
9 Global Federated Learning Solutions Market, By Geography       
 9.1 Introduction      
 9.2 North America      
  9.2.1 US     
  9.2.2 Canada     
  9.2.3 Mexico     
 9.3 Europe      
  9.3.1 Germany     
  9.3.2 UK     
  9.3.3 Italy     
  9.3.4 France     
  9.3.5 Spain     
  9.3.6 Rest of Europe     
 9.4 Asia Pacific      
  9.4.1 Japan     
  9.4.2 China     
  9.4.3 India     
  9.4.4 Australia     
  9.4.5 New Zealand     
  9.4.6 South Korea     
  9.4.7 Rest of Asia Pacific     
 9.5 South America      
  9.5.1 Argentina     
  9.5.2 Brazil     
  9.5.3 Chile     
  9.5.4 Rest of South America     
 9.6 Middle East & Africa      
  9.6.1 Saudi Arabia     
  9.6.2 UAE     
  9.6.3 Qatar     
  9.6.4 South Africa     
  9.6.5 Rest of Middle East & Africa     
        
10 Key Developments       
 10.1 Agreements, Partnerships, Collaborations and Joint Ventures      
 10.2 Acquisitions & Mergers      
 10.3 New Product Launch      
 10.4 Expansions      
 10.5 Other Key Strategies      
        
11 Company Profiling       
 11.1 Microsoft Corporation      
 11.2 DataFleets Ltd      
 11.3 IBM Corporation      
 11.4 Alphabet Inc      
 11.5 Nvidia Corporation      
 11.6 Enveil Inc      
 11.7 Owkin Inc.      
 11.8 Edge Delta Inc      
 11.9 Intellegens Ltd      
 11.10 Secure AI Labs      
 11.11 Cloudera Inc      
 11.12 Sherpa.ai      
        
List of Tables        
1 Global Federated Learning Solutions Market Outlook, By Region (2022-2030) ($MN)       
2 Global Federated Learning Solutions Market Outlook, By Deployment Model (2022-2030) ($MN)       
3 Global Federated Learning Solutions Market Outlook, By Cloud-based (2022-2030) ($MN)       
4 Global Federated Learning Solutions Market Outlook, By On-premises (2022-2030) ($MN)       
5 Global Federated Learning Solutions Market Outlook, By Hybrid (2022-2030) ($MN)       
6 Global Federated Learning Solutions Market Outlook, By Other Deployment Models (2022-2030) ($MN)       
7 Global Federated Learning Solutions Market Outlook, By Organization Size (2022-2030) ($MN)       
8 Global Federated Learning Solutions Market Outlook, By Small and Medium-sized Enterprises (SMEs) (2022-2030) ($MN)       
9 Global Federated Learning Solutions Market Outlook, By Large Enterprises (2022-2030) ($MN)       
10 Global Federated Learning Solutions Market Outlook, By Application (2022-2030) ($MN)       
11 Global Federated Learning Solutions Market Outlook, By Drug Discovery (2022-2030) ($MN)       
12 Global Federated Learning Solutions Market Outlook, By Data Privacy & Security Management (2022-2030) ($MN)       
13 Global Federated Learning Solutions Market Outlook, By Risk Management (2022-2030) ($MN)       
14 Global Federated Learning Solutions Market Outlook, By Shopping Experience Personalization (2022-2030) ($MN)       
15 Global Federated Learning Solutions Market Outlook, By Industrial Internet of Things (2022-2030) ($MN)       
16 Global Federated Learning Solutions Market Outlook, By Online Visual Object Detection (2022-2030) ($MN)       
17 Global Federated Learning Solutions Market Outlook, By Other Applications (2022-2030) ($MN)       
18 Global Federated Learning Solutions Market Outlook, By End User (2022-2030) ($MN)       
19 Global Federated Learning Solutions Market Outlook, By BFSI (2022-2030) ($MN)       
20 Global Federated Learning Solutions Market Outlook, By Healthcare and Life Sciences (2022-2030) ($MN)       
21 Global Federated Learning Solutions Market Outlook, By Retail and E-commerce (2022-2030) ($MN)       
22 Global Federated Learning Solutions Market Outlook, By Manufacturing (2022-2030) ($MN)       
23 Global Federated Learning Solutions Market Outlook, By Energy and Utilities (2022-2030) ($MN)       
24 Global Federated Learning Solutions Market Outlook, By Government (2022-2030) ($MN)       
25 Global Federated Learning Solutions Market Outlook, By Media and Entertainment (2022-2030) ($MN)       
26 Global Federated Learning Solutions Market Outlook, By Telecommunications and Information Technology (IT) (2022-2030) ($MN)       
27 Global Federated Learning Solutions Market Outlook, By Other End Users (2022-2030) ($MN)       
        
Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.        

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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