Insights on the Federated Learning Solutions Global Market to 2027 - Organization's Potential to Leverage Shared Ml Model by Storing Data on Device Presents Opportunities - ResearchAndMarkets.com

DUBLIN--(BUSINESS WIRE)--Sep 12, 2022--

The "Federated Learning Solutions Market Research Report by Vertical, Application, Region - Global Forecast to 2027 - Cumulative Impact of COVID-19" report has been added to ResearchAndMarkets.com's offering.

The Global Federated Learning Solutions Market size was estimated at USD 109.32 million in 2021, USD 125.68 million in 2022, and is projected to grow at a CAGR 15.09% to reach USD 254.13 million by 2027.

Competitive Strategic Window:

The Competitive Strategic Window analyses the competitive landscape in terms of markets, applications, and geographies to help the vendor define an alignment or fit between their capabilities and opportunities for future growth prospects. It describes the optimal or favorable fit for the vendors to adopt successive merger and acquisition strategies, geography expansion, research & development, and new product introduction strategies to execute further business expansion and growth during a forecast period.

FPNV Positioning Matrix:

The FPNV Positioning Matrix evaluates and categorizes the vendors in the Federated Learning Solutions Market based on Business Strategy (Business Growth, Industry Coverage, Financial Viability, and Channel Support) and Product Satisfaction (Value for Money, Ease of Use, Product Features, and Customer Support) that aids businesses in better decision making and understanding the competitive landscape.

Market Share Analysis:

The Market Share Analysis offers the analysis of vendors considering their contribution to the overall market. It provides the idea of its revenue generation into the overall market compared to other vendors in the space. It provides insights into how vendors are performing in terms of revenue generation and customer base compared to others. Knowing market share offers an idea of the size and competitiveness of the vendors for the base year. It reveals the market characteristics in terms of accumulation, fragmentation, dominance, and amalgamation traits.

The report provides insights on the following pointers:

1. Market Penetration: Provides comprehensive information on the market offered by the key players

2. Market Development: Provides in-depth information about lucrative emerging markets and analyze penetration across mature segments of the markets

3. Market Diversification: Provides detailed information about new product launches, untapped geographies, recent developments, and investments

4. Competitive Assessment & Intelligence: Provides an exhaustive assessment of market shares, strategies, products, certification, regulatory approvals, patent landscape, and manufacturing capabilities of the leading players

5. Product Development & Innovation: Provides intelligent insights on future technologies, R&D activities, and breakthrough product developments

The report answers questions such as:

1. What is the market size and forecast of the Global Federated Learning Solutions Market?

2. What are the inhibiting factors and impact of COVID-19 shaping the Global Federated Learning Solutions Market during the forecast period?

3. Which are the products/segments/applications/areas to invest in over the forecast period in the Global Federated Learning Solutions Market?

4. What is the competitive strategic window for opportunities in the Global Federated Learning Solutions Market?

5. What are the technology trends and regulatory frameworks in the Global Federated Learning Solutions Market?

6. What is the market share of the leading vendors in the Global Federated Learning Solutions Market?

7. What modes and strategic moves are considered suitable for entering the Global Federated Learning Solutions Market?

Market Dynamics

Drivers

• Increasing Need for Learning Between Device & Organisation
• Increasing Focus on IoT with Advances in Machine Learning
• Ability to Ensure Better Data Privacy and Security by Training Algorithms on Decentralized Devices

Restraints

• Lack of Skilled Technical Expertise

Opportunities

• Organization's Potential to Leverage Shared Ml Model by Storing Data on Device
• Capability to Enable Predictive Features on Smart Devices Without Impacting User Experience and Privacy

Challenges

• Issue of High Latency and Communication Inefficiency

Companies Mentioned

• Cloudera, Inc.
• Consilient
• DataFleets Ltd.
• Decentralized Machine Learning
• Edge Delta, Inc.
• Enveil, Inc.
• Extreme Vision
• Google LLC by Alphabet Inc.
• Intel Corporation
• Intellegens Limited
• International Business Machines Corporation
• Lifebit
• Microsoft Corporation
• Nvidia Corporation
• Owkin Inc.
• Secure AI Labs
• Sherpa.ai
• WeBank Co., Ltd.

For more information about this report visit https://www.researchandmarkets.com/r/pvl2mx

View source version on businesswire.com:https://www.businesswire.com/news/home/20220912005461/en/

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KEYWORD:

INDUSTRY KEYWORD: SOFTWARE TECHNOLOGY DATA MANAGEMENT

SOURCE: Research and Markets

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