到 2030 年的聯邦學習解決方案市場預測：按部署模型、組織規模、應用程式、最終用戶和地區進行的全球分析

根據 Stratistics MRC 的數據，2024 年全球聯邦學習解決方案市場規模為 1.3749 億美元，預計到 2030 年將達到 2.9237 億美元，預測期內複合年成長率為 13.4%。

聯合學習解決方案代表了機器學習領域的模式轉移，提供了一種跨分散設備和伺服器協作訓練模型的方法，同時確保資料隱私和安全。聯邦學習不是將來自不同來源的原始資料整合到單一伺服器上，而是將模型傳送到進行本地訓練的資料位置。底層資料永遠不會共用。相反，本地訓練的模型被組合起來產生世界模型。此外，該策略在醫療保健和 IT/通訊等行業特別有用，這些行業的安全問題和隱私法使得共用敏感資料變得困難。

世界衛生組織 (WHO) 表示，解決健康的社會決定因素對於改善人群的健康公平和結果至關重要。

物聯網設備的使用增加

由於物聯網 (IoT)，連接設備的數量呈指數級成長，在網路邊緣產生大量資料。從工業感測器到智慧家電，這些小工具會產生有用的資料，您可以使用這些數據來獲得新的觀點並提高生產力。聯合學習提供了一種可擴展的方式來利用這些資料進行機器學習，而不會對網路容量造成負擔。此外，聯邦學習透過在物聯網設備上本地處理資料來實現邊緣的即時分析和決策，減少對中央儲存和大規模資料傳輸的需求。

計算和通訊成本過高

聯邦學習的通訊成本較高，需要大量的處理能力。所有參與設備都需要本地學習，並且可能佔用大量資源，尤其是對於複雜模型。這些規範對於處理能力較低的設備（例如較舊的智慧型手機或物聯網感測器）來說很困難，這可能會導致效能不一致和延遲。此外，在擁有數千或數百萬設備的大型部署中，設備和中央伺服器之間頻繁的通訊以集中模型更新可能會消耗大量頻寬。

重視隱私領域的成長

聯邦學習在資料安全和隱私是關鍵問題的領域（例如醫療保健、金融和法律）呈現出巨大的潛力。透過利用多個診所和醫院的資料，同時保護患者隱私，醫療保健領域的聯合學習可以加速疾病檢測和患者護理預測模型的創建。此外，在金融領域，可以利用多個金融機構的個人財務資料來改善信用評分和詐欺偵測。律師事務所可以使用聯合學習來檢查敏感的法律文件和案例歷史，同時維護客戶的機密。

隱私和安全風險

儘管聯邦學習旨在提高資料隱私，但安全風險仍然存在。攻擊者可以發動各種攻擊，包括成員資格推論和模型反轉，以從共用模型更新中獲取私有資料。此外，惡意參與者可能會在訓練過程中引入受污染的資料或有缺陷的模型更新，這可能會損害結果或降低模型效能。此外，創建和部署強大的防禦（例如安全集中、異常檢測和差異隱私）很重要但很困難。

COVID-19 的影響：

COVID-19 大流行加速了協作學習解決方案的採用。各行各業的機構都致力於利用資料獲得關鍵見解，同時遵守嚴格的資料隱私和安全標準。遠距工作趨勢和對數位基礎設施的日益依賴凸顯了對分散式資料處理技術的需求。此外，迫切需要在不違反隱私法的情況下創建患者結果和病毒傳播的預測模型，並且協作學習在醫療保健行業中引起了極大的興趣。

雲端基礎的細分市場預計將在預測期內成為最大的細分市場

在聯邦學習解決方案市場中，雲端基礎的細分市場佔據最大佔有率。雲端基礎的聯合學習解決方案在成本效益、擴充性和靈活性方面具有多種優勢。透過利用雲端基礎架構強大的處理能力和儲存能力，這些解決方案可以幫助企業有效地處理和處理大量的共同學習挑戰。此外，雲端是聯邦學習的理想環境，特別是對於擁有多個地點的公司來說，因為它能夠跨分散式網路實現順暢的協作和資料共用。

中小企業 (SME) 領域預計在預測期內複合年成長率最高

聯合學習解決方案市場的中小型企業 (SME) 部分預計將以最高的複合年成長率成長。隨著在不犧牲安全性和隱私的情況下對資料主導的洞察力的需求不斷成長，中小型企業擴大採用聯邦學習解決方案。與大型企業相比，中小企業往往缺乏傳統的集中式資料處理所需的廣泛基礎設施和資源。聯合學習為中小型企業提供了一種經濟實惠且可擴展的替代方案，以利用分散資料來利用機器學習的潛力。

比最大的地區

北美在聯邦學習解決方案市場中佔據最大佔有率。這一優勢得益於主要市場參與者的強大存在、新興技術市場以及各行業對最尖端科技的快速採用。聯合學習解決方案的蓬勃發展得益於北美強大的IT基礎設施、有利的法規環境以及對研發的大量投資。此外，除了該地區對資料隱私和安全的重視之外，醫療保健、金融、零售和通訊等行業採用聯邦學習也推動了對個人化服務和預測分析的需求。

複合年成長率最高的地區：

聯合學習解決方案市場預計將以亞太地區最高的複合年成長率成長。快速的數位轉型、擴大採用雲端基礎的技術以及各行業對人工智慧和機器學習的投資增加是推動這一成長的一些因素。中國、印度、日本和韓國等國家在資料分析、物聯網和邊緣運算方面取得了重大進展，推動了對聯邦學習等隱私保護機器學習解決方案的需求。此外，人們對資料隱私和安全的認知不斷提高，政府鼓勵創新和數位化的措施等都有助於擴大亞太地區的市場機會。

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目錄

第1章執行摘要

第2章 前言

• 概述
• 相關利益者
• 調查範圍
• 調查方法
  • 資料探勘
  • 資料分析
  • 資料檢驗
  • 研究途徑
• 研究資訊來源
  • 主要研究資訊來源
  • 二次研究資訊來源
  • 先決條件

第3章市場趨勢分析

• 促進因素
• 抑制因素
• 機會
• 威脅
• 應用分析
• 最終用戶分析
• 新興市場
• COVID-19 的影響

第4章波特五力分析

• 供應商的議價能力
• 買方議價能力
• 替代品的威脅
• 新進入者的威脅
• 競爭公司之間的敵對關係

第5章全球聯邦學習解決方案市場：按部署模型

• 雲端基礎
• 本地
• 混合
• 其他部署模型

第6章全球聯合學習解決方案市場：依組織規模

• 中小企業
• 主要企業

第7章世界聯合會學習解決方案市場：按應用

• 藥物發現
• 資料隱私和安全管理
• 危機管理
• 個人化的購物體驗
• 工業物聯網
• 線上視覺目標檢測
• 其他用途

第8章全球聯合學習解決方案市場：按最終用戶分類

• BFSI
• 醫療保健和生命科學
• 零售與電子商務
• 製造業
• 能源和公共
• 政府
• 媒體和娛樂
• 電訊與資訊科技 (IT)
• 其他最終用戶

第9章全球聯邦學習解決方案市場：按地區

• 北美洲
  • 美國
  • 加拿大
  • 墨西哥
• 歐洲
  • 德國
  • 英國
  • 義大利
  • 法國
  • 西班牙
  • 其他歐洲國家
• 亞太地區
  • 日本
  • 中國
  • 印度
  • 澳洲
  • 紐西蘭
  • 韓國
  • 其他亞太地區
• 南美洲
  • 阿根廷
  • 巴西
  • 智利
  • 南美洲其他地區
• 中東/非洲
  • 沙烏地阿拉伯
  • 阿拉伯聯合大公國
  • 卡達
  • 南非
  • 其他中東和非洲

第10章 主要進展

• 合約、夥伴關係、協作和合資企業
• 收購和合併
• 新產品發布
• 業務擴展
• 其他關鍵策略

第11章 公司概況

• 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

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.