The Asia Pacific Federated Learning Market is expected to witness market growth of 11.7% CAGR during the forecast period (2022-2028).
When two data sets represent the same users but vary in feature space, vertically federated learning can be used. For instance, two businesses in the same city, from which, one is a bank, and the other is an e-commerce store. Because their customer base is expected to probably include the majority of the area's people, the number of common users is likely to be significant. However, because the bank keeps track of the user's expenditure and revenue patterns as well as their credit score, while the e-commerce store keeps track of the user's surfing and purchase history, their user characteristics are vastly different. Vertically federated learning is the practice of aggregating these diverse attributes in an authenticated state and calculating the training loss as well as gradients in a more confidential manner in order to jointly develop a model with both data. Machine learning approaches, such as tree structure models, logistic regression models, and neural network-based designs have all been found to work in this federated system so far.
Due to the rising usage of innovative technologies in numerous industries, the regional demand for federated learning paradigms is increasing. Moreover, the demand for federated learning solutions has also been aided due to the emergence of new technologies such as IoT, AI, and big data analytics to analyze the obtained data in this region. Furthermore, increasing industrialization and continuous data regulation growth in nations such as China, India, and Japan are likely to open up numerous attractive chances for the federated learning market.
Similarly, China's Cyber Security Law and General Principles of the Civil Law were enacted in 2017, stated that internet businesses must not reveal, tamper with, or destroy personal data they gather and that when undergoing data transfers with third parties, they must ensure that the presented contract clearly describes the extent of the data to be exchanged and the data protection obligations. To varying degrees, the implementation of these restrictions offers new hurdles to AI's typical data processing.
The China market dominated the Asia Pacific Federated Learning Market by Country in 2021, and is expected to continue to be a dominant market till 2028; thereby, achieving a market value of $15,118.8 Thousands by 2028. The Japan market is estimated to grow at a CAGR of 11% during (2022 - 2028). Additionally, The India market is expected to showcase a CAGR of 12.4% during (2022 - 2028).
Based on Application, the market is segmented into Drug Discovery, Risk Management, Online Visual Object Detection, Data Privacy & Security Management, Industrial Internet of Things, Augmented Reality/Virtual Reality, Shopping Experience Personalization, and Others. Based on Vertical, the market is segmented into Healthcare & Life Sciences, BFSI, IT & Telecommunication, Energy & Utilities, Manufacturing, Automotive & Transportation, Retail & Ecommerce, and Others. Based on countries, the market is segmented into China, Japan, India, South Korea, Singapore, Malaysia, and Rest of Asia Pacific.
The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include IBM Corporation, Microsoft Corporation, Intel Corporation, Google LLC, Cloudera, Inc., NVIDIA Corporation, Edge Delta, Inc., DataFleets Ltd. (LiveRamp Holdings, Inc.), Enveil, and Secure AI Labs, Inc.
When two data sets represent the same users but vary in feature space, vertically federated learning can be used. For instance, two businesses in the same city, from which, one is a bank, and the other is an e-commerce store. Because their customer base is expected to probably include the majority of the area's people, the number of common users is likely to be significant. However, because the bank keeps track of the user's expenditure and revenue patterns as well as their credit score, while the e-commerce store keeps track of the user's surfing and purchase history, their user characteristics are vastly different. Vertically federated learning is the practice of aggregating these diverse attributes in an authenticated state and calculating the training loss as well as gradients in a more confidential manner in order to jointly develop a model with both data. Machine learning approaches, such as tree structure models, logistic regression models, and neural network-based designs have all been found to work in this federated system so far.
Due to the rising usage of innovative technologies in numerous industries, the regional demand for federated learning paradigms is increasing. Moreover, the demand for federated learning solutions has also been aided due to the emergence of new technologies such as IoT, AI, and big data analytics to analyze the obtained data in this region. Furthermore, increasing industrialization and continuous data regulation growth in nations such as China, India, and Japan are likely to open up numerous attractive chances for the federated learning market.
Similarly, China's Cyber Security Law and General Principles of the Civil Law were enacted in 2017, stated that internet businesses must not reveal, tamper with, or destroy personal data they gather and that when undergoing data transfers with third parties, they must ensure that the presented contract clearly describes the extent of the data to be exchanged and the data protection obligations. To varying degrees, the implementation of these restrictions offers new hurdles to AI's typical data processing.
The China market dominated the Asia Pacific Federated Learning Market by Country in 2021, and is expected to continue to be a dominant market till 2028; thereby, achieving a market value of $15,118.8 Thousands by 2028. The Japan market is estimated to grow at a CAGR of 11% during (2022 - 2028). Additionally, The India market is expected to showcase a CAGR of 12.4% during (2022 - 2028).
Based on Application, the market is segmented into Drug Discovery, Risk Management, Online Visual Object Detection, Data Privacy & Security Management, Industrial Internet of Things, Augmented Reality/Virtual Reality, Shopping Experience Personalization, and Others. Based on Vertical, the market is segmented into Healthcare & Life Sciences, BFSI, IT & Telecommunication, Energy & Utilities, Manufacturing, Automotive & Transportation, Retail & Ecommerce, and Others. Based on countries, the market is segmented into China, Japan, India, South Korea, Singapore, Malaysia, and Rest of Asia Pacific.
The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include IBM Corporation, Microsoft Corporation, Intel Corporation, Google LLC, Cloudera, Inc., NVIDIA Corporation, Edge Delta, Inc., DataFleets Ltd. (LiveRamp Holdings, Inc.), Enveil, and Secure AI Labs, Inc.
Scope of the Study
Market Segments Covered in the Report:
By Application
- Drug Discovery
- Risk Management
- Online Visual Object Detection
- Data Privacy & Security Management
- Industrial Internet of Things
- Augmented Reality/Virtual Reality
- Shopping Experience Personalization
- Others
By Vertical
- Healthcare & Life Sciences
- BFSI
- IT & Telecommunication
- Energy & Utilities
- Manufacturing
- Automotive & Transportation
- Retail & Ecommerce
- Others
By Country
- China
- Japan
- India
- South Korea
- Singapore
- Malaysia
- Rest of Asia Pacific
Key Market Players
List of Companies Profiled in the Report:
- IBM Corporation
- Microsoft Corporation
- Intel Corporation
- Google LLC
- Cloudera, Inc.
- NVIDIA Corporation
- Edge Delta, Inc.
- DataFleets Ltd. (LiveRamp Holdings, Inc.)
- Enveil
- Secure AI Labs, Inc.
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Table of Contents
Chapter 1. Market Scope & Methodology
Chapter 2. Market Overview
Chapter 3. Competition Analysis - Global
Chapter 4. Asia Pacific Federated Learning Market by Application
Chapter 5. Asia Pacific Federated Learning Market by Vertical
Chapter 6. Asia Pacific Federated Learning Market by Country
Chapter 7. Company Profiles
Companies Mentioned
- IBM Corporation
- Microsoft Corporation
- Intel Corporation
- Google LLC
- Cloudera, Inc.
- NVIDIA Corporation
- Edge Delta, Inc.
- DataFleets Ltd. (LiveRamp Holdings, Inc.)
- Enveil
- Secure AI Labs, Inc.
Methodology
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