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市場調査レポート
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1069244

連合学習ソリューションの世界市場予測(2021年~2028年)

Federated Learning Solutions - Global Market Outlook (2021 - 2028)

出版日: | 発行: Stratistics Market Research Consulting | ページ情報: 英文 200+ Pages | 納期: 2~3営業日

価格
価格表記: USDを日本円(税抜)に換算
本日の銀行送金レート: 1USD=136.22円
連合学習ソリューションの世界市場予測(2021年~2028年)
出版日: 2022年03月01日
発行: Stratistics Market Research Consulting
ページ情報: 英文 200+ Pages
納期: 2~3営業日
  • 全表示
  • 概要
  • 図表
  • 目次
概要

世界の連合学習ソリューションの市場規模は、2021年に9,428万米ドルとなり、2028年までには2億2,736万米ドルに達し、予測期間中に13.4%のCAGRで成長すると予測されています。

当レポートでは、世界の連合学習ソリューション市場について調査分析し、市場動向、競合情勢、セグメント別・地域別の市場分析、主要企業のプロファイルなど、体系的な情報を提供しています。

目次

第1章 エグゼクティブサマリー

第2章 序文

第3章 市場動向分析

  • イントロダクション
  • 促進要因
  • 抑制要因
  • 市場機会
  • 脅威
  • 用途分析
  • エンドユーザー分析
  • 新興市場
  • COVID-19の影響

第4章 ポーターのファイブフォース分析

第5章 世界の連合学習ソリューション市場:用途別

  • イントロダクション
  • 創薬
  • インダストリアルIoT(IIoT)
  • オンライン視覚オブジェクト検出
  • 危機管理
  • ショッピングエクスペリエンスのパーソナライゼーション
  • データプライバシー・セキュリティ管理
  • その他の用途
    • 異常検出
    • 企業情報技術(IT)
    • ゲノミクス
    • ビデオ分析

第6章 世界の連合学習ソリューション市場:エンドユーザー別

  • イントロダクション
  • 銀行・金融サービス・保険(BFSI)
  • エネルギー・ユーティリティ
  • 医療・ライフサイエンス
  • 製造
  • 小売・Eコマース
  • その他のエンドユーザー
    • 政府
    • メディア・エンターテインメント
    • 通信・情報技術(IT)

第7章 世界の連合学習ソリューション市場:地域別

  • イントロダクション
  • 北米
    • 米国
    • カナダ
    • メキシコ
  • 欧州
    • ドイツ
    • 英国
    • イタリア
    • フランス
    • スペイン
    • その他欧州
  • アジア太平洋
    • 日本
    • 中国
    • インド
    • オーストラリア
    • ニュージーランド
    • 韓国
    • その他アジア太平洋
  • 南米
    • アルゼンチン
    • ブラジル
    • チリ
    • その他南米
  • 中東・アフリカ
    • サウジアラビア
    • アラブ首長国連邦
    • カタール
    • 南アフリカ
    • その他中東・アフリカ

第8章 主な発展

  • 契約、パートナーシップ、提携、共同事業
  • 買収・合併
  • 新製品の発売
  • 事業拡張
  • その他の重要戦略

第9章 企業プロファイル

  • Cloudera
  • Consilient
  • DataFleets
  • Decentralized Machine Learning
  • Edge Delta
  • Enveil
  • Extreme Vision
  • Google
  • IBM
  • Intellegens
  • Lifebit
  • Microsoft
  • NVIDIA
  • Owkin
  • Secure AI Labs
図表

List of Tables

  • Table 1 Global Federated Learning Solutions Market Outlook, By Region (2020-2028) ($MN)
  • Table 2 Global Federated Learning Solutions Market Outlook, By Application (2020-2028) ($MN)
  • Table 3 Global Federated Learning Solutions Market Outlook, By Drug Discovery (2020-2028) ($MN)
  • Table 4 Global Federated Learning Solutions Market Outlook, By Industrial Internet of Things (IIoT) (2020-2028) ($MN)
  • Table 5 Global Federated Learning Solutions Market Outlook, By Online Visual Object Detection (2020-2028) ($MN)
  • Table 6 Global Federated Learning Solutions Market Outlook, By Risk Management (2020-2028) ($MN)
  • Table 7 Global Federated Learning Solutions Market Outlook, By Shopping Experience Personalization (2020-2028) ($MN)
  • Table 8 Global Federated Learning Solutions Market Outlook, By Data Privacy and Security Management (2020-2028) ($MN)
  • Table 9 Global Federated Learning Solutions Market Outlook, By Other Applications (2020-2028) ($MN)
  • Table 10 Global Federated Learning Solutions Market Outlook, By Anomaly Detection (2020-2028) ($MN)
  • Table 11 Global Federated Learning Solutions Market Outlook, By Corporate Information Technology (IT) (2020-2028) ($MN)
  • Table 12 Global Federated Learning Solutions Market Outlook, By Genomics (2020-2028) ($MN)
  • Table 13 Global Federated Learning Solutions Market Outlook, By Video Analytics (2020-2028) ($MN)
  • Table 14 Global Federated Learning Solutions Market Outlook, By End User (2020-2028) ($MN)
  • Table 15 Global Federated Learning Solutions Market Outlook, By Banking, Financial Services and Insurance (BFSI) (2020-2028) ($MN)
  • Table 16 Global Federated Learning Solutions Market Outlook, By Energy and Utilities (2020-2028) ($MN)
  • Table 17 Global Federated Learning Solutions Market Outlook, By Healthcare and Life Sciences (2020-2028) ($MN)
  • Table 18 Global Federated Learning Solutions Market Outlook, By Manufacturing (2020-2028) ($MN)
  • Table 19 Global Federated Learning Solutions Market Outlook, By Retail and E-Commerce (2020-2028) ($MN)
  • Table 20 Global Federated Learning Solutions Market Outlook, By Other End Users (2020-2028) ($MN)
  • Table 21 Global Federated Learning Solutions Market Outlook, By Government (2020-2028) ($MN)
  • Table 22 Global Federated Learning Solutions Market Outlook, By Media and Entertainment (2020-2028) ($MN)
  • Table 23 Global Federated Learning Solutions Market Outlook, By Telecommunications and Information Technology (IT) (2020-2028) ($MN)
  • Table 24 North America Federated Learning Solutions Market Outlook, By Country (2020-2028) ($MN)
  • Table 25 North America Federated Learning Solutions Market Outlook, By Application (2020-2028) ($MN)
  • Table 26 North America Federated Learning Solutions Market Outlook, By Drug Discovery (2020-2028) ($MN)
  • Table 27 North America Federated Learning Solutions Market Outlook, By Industrial Internet of Things (IIoT) (2020-2028) ($MN)
  • Table 28 North America Federated Learning Solutions Market Outlook, By Online Visual Object Detection (2020-2028) ($MN)
  • Table 29 North America Federated Learning Solutions Market Outlook, By Risk Management (2020-2028) ($MN)
  • Table 30 North America Federated Learning Solutions Market Outlook, By Shopping Experience Personalization (2020-2028) ($MN)
  • Table 31 North America Federated Learning Solutions Market Outlook, By Data Privacy and Security Management (2020-2028) ($MN)
  • Table 32 North America Federated Learning Solutions Market Outlook, By Other Applications (2020-2028) ($MN)
  • Table 33 North America Federated Learning Solutions Market Outlook, By Anomaly Detection (2020-2028) ($MN)
  • Table 34 North America Federated Learning Solutions Market Outlook, By Corporate Information Technology (IT) (2020-2028) ($MN)
  • Table 35 North America Federated Learning Solutions Market Outlook, By Genomics (2020-2028) ($MN)
  • Table 36 North America Federated Learning Solutions Market Outlook, By Video Analytics (2020-2028) ($MN)
  • Table 37 North America Federated Learning Solutions Market Outlook, By End User (2020-2028) ($MN)
  • Table 38 North America Federated Learning Solutions Market Outlook, By Banking, Financial Services and Insurance (BFSI) (2020-2028) ($MN)
  • Table 39 North America Federated Learning Solutions Market Outlook, By Energy and Utilities (2020-2028) ($MN)
  • Table 40 North America Federated Learning Solutions Market Outlook, By Healthcare and Life Sciences (2020-2028) ($MN)
  • Table 41 North America Federated Learning Solutions Market Outlook, By Manufacturing (2020-2028) ($MN)
  • Table 42 North America Federated Learning Solutions Market Outlook, By Retail and E-Commerce (2020-2028) ($MN)
  • Table 43 North America Federated Learning Solutions Market Outlook, By Other End Users (2020-2028) ($MN)
  • Table 44 North America Federated Learning Solutions Market Outlook, By Government (2020-2028) ($MN)
  • Table 45 North America Federated Learning Solutions Market Outlook, By Media and Entertainment (2020-2028) ($MN)
  • Table 46 North America Federated Learning Solutions Market Outlook, By Telecommunications and Information Technology (IT) (2020-2028) ($MN)
  • Table 47 Europe Federated Learning Solutions Market Outlook, By Country (2020-2028) ($MN)
  • Table 48 Europe Federated Learning Solutions Market Outlook, By Application (2020-2028) ($MN)
  • Table 49 Europe Federated Learning Solutions Market Outlook, By Drug Discovery (2020-2028) ($MN)
  • Table 50 Europe Federated Learning Solutions Market Outlook, By Industrial Internet of Things (IIoT) (2020-2028) ($MN)
  • Table 51 Europe Federated Learning Solutions Market Outlook, By Online Visual Object Detection (2020-2028) ($MN)
  • Table 52 Europe Federated Learning Solutions Market Outlook, By Risk Management (2020-2028) ($MN)
  • Table 53 Europe Federated Learning Solutions Market Outlook, By Shopping Experience Personalization (2020-2028) ($MN)
  • Table 54 Europe Federated Learning Solutions Market Outlook, By Data Privacy and Security Management (2020-2028) ($MN)
  • Table 55 Europe Federated Learning Solutions Market Outlook, By Other Applications (2020-2028) ($MN)
  • Table 56 Europe Federated Learning Solutions Market Outlook, By Anomaly Detection (2020-2028) ($MN)
  • Table 57 Europe Federated Learning Solutions Market Outlook, By Corporate Information Technology (IT) (2020-2028) ($MN)
  • Table 58 Europe Federated Learning Solutions Market Outlook, By Genomics (2020-2028) ($MN)
  • Table 59 Europe Federated Learning Solutions Market Outlook, By Video Analytics (2020-2028) ($MN)
  • Table 60 Europe Federated Learning Solutions Market Outlook, By End User (2020-2028) ($MN)
  • Table 61 Europe Federated Learning Solutions Market Outlook, By Banking, Financial Services and Insurance (BFSI) (2020-2028) ($MN)
  • Table 62 Europe Federated Learning Solutions Market Outlook, By Energy and Utilities (2020-2028) ($MN)
  • Table 63 Europe Federated Learning Solutions Market Outlook, By Healthcare and Life Sciences (2020-2028) ($MN)
  • Table 64 Europe Federated Learning Solutions Market Outlook, By Manufacturing (2020-2028) ($MN)
  • Table 65 Europe Federated Learning Solutions Market Outlook, By Retail and E-Commerce (2020-2028) ($MN)
  • Table 66 Europe Federated Learning Solutions Market Outlook, By Other End Users (2020-2028) ($MN)
  • Table 67 Europe Federated Learning Solutions Market Outlook, By Government (2020-2028) ($MN)
  • Table 68 Europe Federated Learning Solutions Market Outlook, By Media and Entertainment (2020-2028) ($MN)
  • Table 69 Europe Federated Learning Solutions Market Outlook, By Telecommunications and Information Technology (IT) (2020-2028) ($MN)
  • Table 70 Asia Pacific Federated Learning Solutions Market Outlook, By Country (2020-2028) ($MN)
  • Table 71 Asia Pacific Federated Learning Solutions Market Outlook, By Application (2020-2028) ($MN)
  • Table 72 Asia Pacific Federated Learning Solutions Market Outlook, By Drug Discovery (2020-2028) ($MN)
  • Table 73 Asia Pacific Federated Learning Solutions Market Outlook, By Industrial Internet of Things (IIoT) (2020-2028) ($MN)
  • Table 74 Asia Pacific Federated Learning Solutions Market Outlook, By Online Visual Object Detection (2020-2028) ($MN)
  • Table 75 Asia Pacific Federated Learning Solutions Market Outlook, By Risk Management (2020-2028) ($MN)
  • Table 76 Asia Pacific Federated Learning Solutions Market Outlook, By Shopping Experience Personalization (2020-2028) ($MN)
  • Table 77 Asia Pacific Federated Learning Solutions Market Outlook, By Data Privacy and Security Management (2020-2028) ($MN)
  • Table 78 Asia Pacific Federated Learning Solutions Market Outlook, By Other Applications (2020-2028) ($MN)
  • Table 79 Asia Pacific Federated Learning Solutions Market Outlook, By Anomaly Detection (2020-2028) ($MN)
  • Table 80 Asia Pacific Federated Learning Solutions Market Outlook, By Corporate Information Technology (IT) (2020-2028) ($MN)
  • Table 81 Asia Pacific Federated Learning Solutions Market Outlook, By Genomics (2020-2028) ($MN)
  • Table 82 Asia Pacific Federated Learning Solutions Market Outlook, By Video Analytics (2020-2028) ($MN)
  • Table 83 Asia Pacific Federated Learning Solutions Market Outlook, By End User (2020-2028) ($MN)
  • Table 84 Asia Pacific Federated Learning Solutions Market Outlook, By Banking, Financial Services and Insurance (BFSI) (2020-2028) ($MN)
  • Table 85 Asia Pacific Federated Learning Solutions Market Outlook, By Energy and Utilities (2020-2028) ($MN)
  • Table 86 Asia Pacific Federated Learning Solutions Market Outlook, By Healthcare and Life Sciences (2020-2028) ($MN)
  • Table 87 Asia Pacific Federated Learning Solutions Market Outlook, By Manufacturing (2020-2028) ($MN)
  • Table 88 Asia Pacific Federated Learning Solutions Market Outlook, By Retail and E-Commerce (2020-2028) ($MN)
  • Table 89 Asia Pacific Federated Learning Solutions Market Outlook, By Other End Users (2020-2028) ($MN)
  • Table 90 Asia Pacific Federated Learning Solutions Market Outlook, By Government (2020-2028) ($MN)
  • Table 91 Asia Pacific Federated Learning Solutions Market Outlook, By Media and Entertainment (2020-2028) ($MN)
  • Table 92 Asia Pacific Federated Learning Solutions Market Outlook, By Telecommunications and Information Technology (IT) (2020-2028) ($MN)
  • Table 93 South America Federated Learning Solutions Market Outlook, By Country (2020-2028) ($MN)
  • Table 94 South America Federated Learning Solutions Market Outlook, By Application (2020-2028) ($MN)
  • Table 95 South America Federated Learning Solutions Market Outlook, By Drug Discovery (2020-2028) ($MN)
  • Table 96 South America Federated Learning Solutions Market Outlook, By Industrial Internet of Things (IIoT) (2020-2028) ($MN)
  • Table 97 South America Federated Learning Solutions Market Outlook, By Online Visual Object Detection (2020-2028) ($MN)
  • Table 98 South America Federated Learning Solutions Market Outlook, By Risk Management (2020-2028) ($MN)
  • Table 99 South America Federated Learning Solutions Market Outlook, By Shopping Experience Personalization (2020-2028) ($MN)
  • Table 100 South America Federated Learning Solutions Market Outlook, By Data Privacy and Security Management (2020-2028) ($MN)
  • Table 101 South America Federated Learning Solutions Market Outlook, By Other Applications (2020-2028) ($MN)
  • Table 102 South America Federated Learning Solutions Market Outlook, By Anomaly Detection (2020-2028) ($MN)
  • Table 103 South America Federated Learning Solutions Market Outlook, By Corporate Information Technology (IT) (2020-2028) ($MN)
  • Table 104 South America Federated Learning Solutions Market Outlook, By Genomics (2020-2028) ($MN)
  • Table 105 South America Federated Learning Solutions Market Outlook, By Video Analytics (2020-2028) ($MN)
  • Table 106 South America Federated Learning Solutions Market Outlook, By End User (2020-2028) ($MN)
  • Table 107 South America Federated Learning Solutions Market Outlook, By Banking, Financial Services and Insurance (BFSI) (2020-2028) ($MN)
  • Table 108 South America Federated Learning Solutions Market Outlook, By Energy and Utilities (2020-2028) ($MN)
  • Table 109 South America Federated Learning Solutions Market Outlook, By Healthcare and Life Sciences (2020-2028) ($MN)
  • Table 110 South America Federated Learning Solutions Market Outlook, By Manufacturing (2020-2028) ($MN)
  • Table 111 South America Federated Learning Solutions Market Outlook, By Retail and E-Commerce (2020-2028) ($MN)
  • Table 112 South America Federated Learning Solutions Market Outlook, By Other End Users (2020-2028) ($MN)
  • Table 113 South America Federated Learning Solutions Market Outlook, By Government (2020-2028) ($MN)
  • Table 114 South America Federated Learning Solutions Market Outlook, By Media and Entertainment (2020-2028) ($MN)
  • Table 115 South America Federated Learning Solutions Market Outlook, By Telecommunications and Information Technology (IT) (2020-2028) ($MN)
  • Table 116 Middle East & Africa Federated Learning Solutions Market Outlook, By Country (2020-2028) ($MN)
  • Table 117 Middle East & Africa Federated Learning Solutions Market Outlook, By Application (2020-2028) ($MN)
  • Table 118 Middle East & Africa Federated Learning Solutions Market Outlook, By Drug Discovery (2020-2028) ($MN)
  • Table 119 Middle East & Africa Federated Learning Solutions Market Outlook, By Industrial Internet of Things (IIoT) (2020-2028) ($MN)
  • Table 120 Middle East & Africa Federated Learning Solutions Market Outlook, By Online Visual Object Detection (2020-2028) ($MN)
  • Table 121 Middle East & Africa Federated Learning Solutions Market Outlook, By Risk Management (2020-2028) ($MN)
  • Table 122 Middle East & Africa Federated Learning Solutions Market Outlook, By Shopping Experience Personalization (2020-2028) ($MN)
  • Table 123 Middle East & Africa Federated Learning Solutions Market Outlook, By Data Privacy and Security Management (2020-2028) ($MN)
  • Table 124 Middle East & Africa Federated Learning Solutions Market Outlook, By Other Applications (2020-2028) ($MN)
  • Table 125 Middle East & Africa Federated Learning Solutions Market Outlook, By Anomaly Detection (2020-2028) ($MN)
  • Table 126 Middle East & Africa Federated Learning Solutions Market Outlook, By Corporate Information Technology (IT) (2020-2028) ($MN)
  • Table 127 Middle East & Africa Federated Learning Solutions Market Outlook, By Genomics (2020-2028) ($MN)
  • Table 128 Middle East & Africa Federated Learning Solutions Market Outlook, By Video Analytics (2020-2028) ($MN)
  • Table 129 Middle East & Africa Federated Learning Solutions Market Outlook, By End User (2020-2028) ($MN)
  • Table 130 Middle East & Africa Federated Learning Solutions Market Outlook, By Banking, Financial Services and Insurance (BFSI) (2020-2028) ($MN)
  • Table 131 Middle East & Africa Federated Learning Solutions Market Outlook, By Energy and Utilities (2020-2028) ($MN)
  • Table 132 Middle East & Africa Federated Learning Solutions Market Outlook, By Healthcare and Life Sciences (2020-2028) ($MN)
  • Table 133 Middle East & Africa Federated Learning Solutions Market Outlook, By Manufacturing (2020-2028) ($MN)
  • Table 134 Middle East & Africa Federated Learning Solutions Market Outlook, By Retail and E-Commerce (2020-2028) ($MN)
  • Table 135 Middle East & Africa Federated Learning Solutions Market Outlook, By Other End Users (2020-2028) ($MN)
  • Table 136 Middle East & Africa Federated Learning Solutions Market Outlook, By Government (2020-2028) ($MN)
  • Table 137 Middle East & Africa Federated Learning Solutions Market Outlook, By Media and Entertainment (2020-2028) ($MN)
  • Table 138 Middle East & Africa Federated Learning Solutions Market Outlook, By Telecommunications and Information Technology (IT) (2020-2028) ($MN)
目次
Product Code: SMRC20778

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
    • 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 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
    • 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 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