查看购物车
  • Traditional Chinese
  • English
  • Japanese
  • Korean
封面
市场调查报告书
商品编码
1503319

到 2030 年的联邦学习解决方案市场预测：按部署模型、组织规模、应用程式、最终用户和地区进行的全球分析

Federated Learning Solutions Market Forecasts to 2030 - Global Analysis By Deployment Model, Organization Size (Small and Medium-sized Enterprises and Large Enterprises), Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的数据，2024 年全球联邦学习解决方案市场规模为 1.3749 亿美元，预计到 2030 年将达到 2.9237 亿美元，预测期内复合年增长率为 13.4%。

联合学习解决方案代表了机器学习领域的模式转移，提供了一种跨分散设备和伺服器协作训练模型的方法，同时确保资料隐私和安全。联邦学习不是将来自不同来源的原始资料整合到单一伺服器上，而是将模型传送到进行本地训练的资料位置。底层资料永远不会共用。相反，本地训练的模型被组合起来产生世界模型。此外，该策略在医疗保健和 IT/通讯等行业特别有用，这些行业的安全问题和隐私法使得共用敏感资料变得困难。

世界卫生组织 (WHO) 表示，解决健康的社会决定因素对于改善人群的健康公平和结果至关重要。

物联网设备的使用增加

由于物联网 (IoT)，连接设备的数量呈指数级增长，在网路边缘产生大量资料。从工业感测器到智慧家电，这些小工具会产生有用的资料，您可以使用这些数据来获得新的观点并提高生产力。联合学习提供了一种可扩展的方式来利用这些资料进行机器学习，而不会对网路容量造成负担。此外，联邦学习透过在物联网设备上本地处理资料来实现边缘的即时分析和决策，减少对中央储存和大规模资料传输的需求。

计算和通讯成本过高

联邦学习的通讯成本较高，需要大量的处理能力。所有参与设备都需要本地学习，并且可能占用大量资源，尤其是对于复杂模型。这些规范对于处理能力较低的设备（例如较旧的智慧型手机或物联网感测器）来说很困难，这可能会导致效能不一致和延迟。此外，在拥有数千或数百万设备的大型部署中，设备和中央伺服器之间频繁的通讯以集中模型更新可能会消耗大量频宽。

重视隐私领域的成长

联邦学习在资料安全和隐私是关键问题的领域（例如医疗保健、金融和法律）呈现出巨大的潜力。透过利用多个诊所和医院的资料，同时保护患者隐私，医疗保健领域的联合学习可以加速疾病检测和患者护理预测模型的创建。此外，在金融领域，可以利用多个金融机构的个人财务资料来改善信用评分和诈欺侦测。律师事务所可以使用联合学习来检查敏感的法律文件和案例历史，同时维护客户的机密。

隐私和安全风险

儘管联邦学习旨在提高资料隐私，但安全风险仍然存在。攻击者可以发动各种攻击，包括成员资格推论和模型反转，以从共用模型更新中获取私有资料。此外，恶意参与者可能会在训练过程中引入受污染的资料或有缺陷的模型更新，这可能会损害结果或降低模型效能。此外，创建和部署强大的防御（例如安全集中、异常检测和差异隐私）很重要但很困难。

COVID-19 的影响：

COVID-19 大流行加速了协作学习解决方案的采用。各行各业的机构都致力于利用资料获得关键见解，同时遵守严格的资料隐私和安全标准。远距工作趋势和对数位基础设施的日益依赖凸显了对分散式资料处理技术的需求。此外，迫切需要在不违反隐私法的情况下创建患者结果和病毒传播的预测模型，并且协作学习在医疗保健行业中引起了极大的兴趣。

云端基础的细分市场预计将在预测期内成为最大的细分市场

在联邦学习解决方案市场中，云端基础的细分市场占据最大份额。云端基础的联合学习解决方案在成本效益、扩充性和灵活性方面具有多种优势。透过利用云端基础架构强大的处理能力和储存能力，这些解决方案可以帮助企业有效地处理和处理大量的共同学习挑战。此外，云端是联邦学习的理想环境，特别是对于拥有多个地点的公司来说，因为它能够跨分散式网路实现顺畅的协作和资料共用。

中小企业 (SME) 领域预计在预测期内复合年增长率最高

联合学习解决方案市场的中小型企业 (SME) 部分预计将以最高的复合年增长率成长。随着在不牺牲安全性和隐私的情况下对资料主导的洞察力的需求不断增长，中小型企业越来越多地采用联邦学习解决方案。与大型企业相比，中小企业往往缺乏传统的集中式资料处理所需的广泛基础设施和资源。联合学习为中小型企业提供了一种经济实惠且可扩展的替代方案，以利用分散资料来利用机器学习的潜力。

比最大的地区

北美在联邦学习解决方案市场中占据最大份额。这一优势得益于主要市场参与者的强大存在、新兴技术市场以及各行业对最尖端科技的快速采用。联合学习解决方案的蓬勃发展得益于北美强大的IT基础设施、有利的法规环境以及对研发的大量投资。此外，除了该地区对资料隐私和安全的重视之外，医疗保健、金融、零售和通讯等行业采用联邦学习也推动了对个人化服务和预测分析的需求。

复合年增长率最高的地区：

联合学习解决方案市场预计将以亚太地区最高的复合年增长率成长。快速的数位转型、越来越多地采用云端基础的技术以及各行业对人工智慧和机器学习的投资增加是推动这一成长的一些因素。中国、印度、日本和韩国等国家在资料分析、物联网和边缘运算方面取得了重大进展，推动了对联邦学习等隐私保护机器学习解决方案的需求。此外，人们对资料隐私和安全的认识不断提高，政府鼓励创新和数数位化的措施等都有助于扩大亚太地区的市场机会。

免费客製化服务

订阅此报告的客户可以存取以下免费自订选项之一：

  • 公司简介
    • 其他市场参与者的综合分析（最多 3 家公司）
    • 主要企业SWOT分析（最多3家企业）
  • 区域分割
    • 根据客户兴趣对主要国家的市场估计、预测和复合年增长率（註：基于可行性检查）
  • 竞争基准化分析
    • 根据产品系列、地理分布和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 前言

  • 概述
  • 相关利益者
  • 调查范围
  • 调查方法
    • 资料探勘
    • 资料分析
    • 资料检验
    • 研究途径
  • 研究资讯来源
    • 主要研究资讯来源
    • 二次研究资讯来源
    • 先决条件

第三章市场趋势分析

  • 促进因素
  • 抑制因素
  • 机会
  • 威胁
  • 应用分析
  • 最终用户分析
  • 新兴市场
  • COVID-19 的影响

第四章波特五力分析

  • 供应商的议价能力
  • 买方议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争公司之间的敌对关係

第五章全球联邦学习解决方案市场：按部署模型

  • 云端基础
  • 本地
  • 混合
  • 其他部署模型

第六章全球联合学习解决方案市场：依组织规模

  • 中小企业
  • 大公司

第七章世界联合会学习解决方案市场：按应用

  • 药物发现
  • 资料隐私和安全管理
  • 危机管理
  • 个人化的购物体验
  • 工业物联网
  • 线上视觉目标检测
  • 其他用途

第八章全球联合学习解决方案市场：按最终用户分类

  • BFSI
  • 医疗保健和生命科学
  • 零售与电子商务
  • 製造业
  • 能源和公共
  • 政府
  • 媒体和娱乐
  • 电讯与资讯科技 (IT)
  • 其他最终用户

第九章全球联邦学习解决方案市场：按地区

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲国家
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 中东/非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲

第10章 主要进展

  • 合约、伙伴关係、协作和合资企业
  • 收购和合併
  • 新产品发布
  • 业务扩展
  • 其他关键策略

第十一章 公司概况

  • Microsoft Corporation
  • DataFleets Ltd
  • IBM Corporation
  • Alphabet Inc
  • Nvidia Corporation
  • Enveil Inc
  • Owkin Inc.
  • Edge Delta Inc
  • Intellegens Ltd
  • Secure AI Labs
  • Cloudera Inc
  • Sherpa.ai
Product Code: SMRC26485

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
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • 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
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • 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

  • Table 1 Global Federated Learning Solutions Market Outlook, By Region (2022-2030) ($MN)
  • Table 2 Global Federated Learning Solutions Market Outlook, By Deployment Model (2022-2030) ($MN)
  • Table 3 Global Federated Learning Solutions Market Outlook, By Cloud-based (2022-2030) ($MN)
  • Table 4 Global Federated Learning Solutions Market Outlook, By On-premises (2022-2030) ($MN)
  • Table 5 Global Federated Learning Solutions Market Outlook, By Hybrid (2022-2030) ($MN)
  • Table 6 Global Federated Learning Solutions Market Outlook, By Other Deployment Models (2022-2030) ($MN)
  • Table 7 Global Federated Learning Solutions Market Outlook, By Organization Size (2022-2030) ($MN)
  • Table 8 Global Federated Learning Solutions Market Outlook, By Small and Medium-sized Enterprises (SMEs) (2022-2030) ($MN)
  • Table 9 Global Federated Learning Solutions Market Outlook, By Large Enterprises (2022-2030) ($MN)
  • Table 10 Global Federated Learning Solutions Market Outlook, By Application (2022-2030) ($MN)
  • Table 11 Global Federated Learning Solutions Market Outlook, By Drug Discovery (2022-2030) ($MN)
  • Table 12 Global Federated Learning Solutions Market Outlook, By Data Privacy & Security Management (2022-2030) ($MN)
  • Table 13 Global Federated Learning Solutions Market Outlook, By Risk Management (2022-2030) ($MN)
  • Table 14 Global Federated Learning Solutions Market Outlook, By Shopping Experience Personalization (2022-2030) ($MN)
  • Table 15 Global Federated Learning Solutions Market Outlook, By Industrial Internet of Things (2022-2030) ($MN)
  • Table 16 Global Federated Learning Solutions Market Outlook, By Online Visual Object Detection (2022-2030) ($MN)
  • Table 17 Global Federated Learning Solutions Market Outlook, By Other Applications (2022-2030) ($MN)
  • Table 18 Global Federated Learning Solutions Market Outlook, By End User (2022-2030) ($MN)
  • Table 19 Global Federated Learning Solutions Market Outlook, By BFSI (2022-2030) ($MN)
  • Table 20 Global Federated Learning Solutions Market Outlook, By Healthcare and Life Sciences (2022-2030) ($MN)
  • Table 21 Global Federated Learning Solutions Market Outlook, By Retail and E-commerce (2022-2030) ($MN)
  • Table 22 Global Federated Learning Solutions Market Outlook, By Manufacturing (2022-2030) ($MN)
  • Table 23 Global Federated Learning Solutions Market Outlook, By Energy and Utilities (2022-2030) ($MN)
  • Table 24 Global Federated Learning Solutions Market Outlook, By Government (2022-2030) ($MN)
  • Table 25 Global Federated Learning Solutions Market Outlook, By Media and Entertainment (2022-2030) ($MN)
  • Table 26 Global Federated Learning Solutions Market Outlook, By Telecommunications and Information Technology (IT) (2022-2030) ($MN)
  • Table 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.