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1889202

全球聯邦學習市場：預測（至 2032 年）—按組件、部署方法、學習類型、通訊模式、用例、組織規模和地區進行分析

Federated Learning Market Forecasts to 2032 - Global Analysis By Component (Software and Services), Deployment Mode, Learning Type, Communication Pattern, Application, Organization Size and By Geography

出版日期: 2025年12月12日 | 出版商:

Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

簡介目錄圖表

根據 Stratistics MRC 的一項研究，預計到 2025 年，全球聯邦學習市場規模將達到 1.6133 億美元，到 2032 年將達到 4.6707 億美元，在預測期內複合年成長率為 16.4%。

聯邦學習是一種協作式訓練技術，它允許多個設備或節點建立一個通用的機器學習模型，同時將原始資料保留在本地。這種方法無需將敏感資訊傳輸到中央伺服器，只需傳輸並安全地聚合已處理的模型參數即可。它增強了資料隱私，降低了通訊開銷，並支援從分散式資料來源學習。在智慧型手機、醫療保健系統、銀行和連網設備等領域，保護個人資訊至關重要，因此聯邦學習尤其有用。

對協作人工智慧的需求日益成長

各組織機構正日益尋求在不損害隱私的前提下，利用分散式資料訓練模型的方法。聯邦學習允許多方協作建構共用智慧，同時保持敏感資料集的去中心化。這種協作方式在醫療保健、金融和通訊等領域變得至關重要。邊緣設備和安全運算的進步進一步強化了這一趨勢。隨著各行業努力建構可擴展且保護隱私的人工智慧生態系統，對聯邦學習的需求持續成長。

通訊開銷高

客戶端和伺服器之間頻繁的資料交換會降低處理速度並增加網路資源壓力。大規模的模型規模和不可靠的連結會加劇這個挑戰。目前，各組織機構正被鼓勵投資於最佳化的通訊協定，以降低延遲並提高同步效率。諸如模型壓縮和自適應更新規則等技術正在被探索用於應對這一挑戰。儘管取得了這些進展，通訊效率低下仍然是限制其廣泛應用的持續性阻礙因素。

與區塊鏈和安全計算的整合

區塊鏈為共用模型更新增添了透明度和防篡改性，從而增強了參與者之間的信任。同態加密和差分隱私等安全運算技術確保了分散式網路中的機密性。這些技術的結合使得以往不願共用資料的組織之間能夠進行安全協作。新興框架著重於去中心化管治、智慧合約和自動化信任檢驗。這種融合有望顯著擴展聯邦學習在受監管行業中的應用場景。

缺乏標準化和互通性

不同平台通常使用不相容的框架，限制了無縫協作。這種分散化減緩了技術的普及，並使其難以與現有人工智慧工作流程整合。缺乏統一的通訊協定增加了開發人員和企業的技術難度。產業協會和研究機構正在努力製定通用準則，但進展緩慢。在標準成熟之前，互通性問題將繼續阻礙聯邦學習解決方案的可擴展性。

新冠疫情的感染疾病：

新冠疫情加速了跨產業、保護隱私的資料協作需求。醫療機構尤其採用聯邦學習技術來分析病患數據，同時避免洩漏敏感資訊。全球業務中斷也促使企業更加依賴分散式系統來降低資料共用風險。遠距辦公環境促使企業考慮採用可在多種裝置上運行的分散式人工智慧模型。這次危機凸顯了安全協作分析的重要性，並激發了人們對聯邦學習研究的興趣。

在預測期內，解決方案領域將佔據最大的市場佔有率。

預計在預測期內，解決方案領域將佔據最大的市場佔有率，這主要得益於企業對可簡化分散式訓練的即用型部署平台的需求不斷成長。這些解決方案提供內建的安全性、模型管理和編配功能。金融、醫療保健和零售業的企業更傾向於選擇綜合軟體套件，而非客製化開發。此外，日益成長的資料隱私合規需求也進一步推動了打包式聯邦學習解決方案的普及。

在預測期內，汽車產業將實現最高的複合年成長率。

預計在預測期內，汽車產業將實現最高成長率，因為聯網汽車和自動駕駛系統的日益普及推動了對協同模型訓練的需求。聯邦學習使汽車製造商能夠利用車輛產生的數據，而無需將其傳輸到中央伺服器。這既增強了即時決策能力，也保障了使用者隱私。應用範例包括駕駛員行為建模、預測性維護和進階感知系統。

佔比最大的地區：

預計北美將在預測期內佔據最大的市場佔有率。強大的技術基礎設施和對先進人工智慧框架的早期應用支撐了這一主導地位。該地區對資料隱私的監管重視正在推動企業採用聯邦學習技術。領先的科技公司和研究機構持續增加對去中心化人工智慧技術研發的投入。產業合作和政府主導的措施也進一步促進了市場成長。

年複合成長率最高的地區：

預計亞太地區在預測期內將實現最高的複合年成長率。快速的數位化、不斷擴展的行動生態系統以及對人工智慧的大力投資將推動這一成長。中國、日本、韓國和印度等國家正積極探索用於大規模應用的去中心化人工智慧模式。醫療保健、零售和製造業等行業的公司正在採用隱私保護技術來處理大量資料集。政府支持人工智慧創新的措施也進一步增強了該地區的發展動能。

免費客製化服務：

訂閱本報告的用戶可從以下免費自訂選項中選擇一項：

公司簡介
- 對最多三家其他公司進行全面分析
- 對主要企業進行SWOT分析（最多3家公司）
區域分類
- 根據客戶興趣對主要國家進行市場估算、預測和複合年成長率分析（註：基於可行性檢查）
競爭基準化分析
- 基於產品系列、地域覆蓋和策略聯盟對主要企業基準化分析

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
亞太地區
- 日本
- 中國
- 印度
- 澳洲
- 紐西蘭
- 韓國
- 亞太其他地區
南美洲
- 阿根廷
- 巴西
- 智利
- 其他南美國家
中東和非洲
- 沙烏地阿拉伯
- 阿拉伯聯合大公國
- 卡達
- 南非
- 其他中東和非洲地區

第12章：主要趨勢

合約、商業夥伴關係和合資企業
企業合併（M&A）
新產品上市
業務拓展
其他關鍵策略

第13章：企業概況

Google
Intellegens
Apple
Sherpa.ai
NVIDIA
Secure AI Labs
Microsoft
DataFleets
IBM
Enveil
Intel
Lifebit
Cloudera
Flower
Owkin

簡介目錄圖表

Product Code: SMRC32662

According to Stratistics MRC, the Global Federated Learning Market is accounted for $161.33 million in 2025 and is expected to reach $467.07 million by 2032 growing at a CAGR of 16.4% during the forecast period. Federated Learning is a collaborative training technique that allows many devices or nodes to build a common machine learning model while keeping their original data stored locally. Rather than moving sensitive information to a central server, only processed model parameters are sent for secure aggregation. This approach strengthens data privacy, lowers communication overhead, and supports learning from dispersed data sources. It is especially useful in areas like smartphones, medical systems, banking, and connected devices where protecting personal information is critical.

Market Dynamics:

Driver:

Rising demand for collaborative AI

Organizations are increasingly seeking ways to train models using distributed data without compromising privacy. Federated learning enables multiple entities to work together on shared intelligence while keeping sensitive datasets decentralized. This collaborative approach is becoming vital across sectors like healthcare, finance, and telecommunications. Advancements in edge devices and secure computation have further strengthened this trend. As industries aim for scalable, privacy-preserving AI ecosystems, the demand for federated learning continues to surge.

Restraint:

High communication overhead

Frequent data exchanges between clients and servers can slow down processes and strain network resources. This challenge becomes more evident when dealing with large model sizes or unstable connectivity environments. Organizations must invest in optimized communication protocols to reduce latency and improve synchronization. Techniques such as model compression and adaptive update rules are being explored to address the issue. Despite these advancements, communication inefficiency remains a persistent constraint for widespread deployment.

Opportunity:

Integration with blockchain and secure computing

Blockchain adds transparency and tamper-resistance to shared model updates, enhancing trust among participants. Secure computing techniques like homomorphic encryption and differential privacy strengthen confidentiality across decentralized networks. These combined technologies enable safer collaboration between organizations that would otherwise hesitate to share data. Emerging frameworks are focusing on decentralized governance, smart contracts, and automated trust verification. This convergence could significantly expand federated learning use cases across regulated industries.

Threat:

Lack of standardization and interoperability

Different platforms often use incompatible frameworks, limiting seamless collaboration. This fragmentation slows adoption and complicates integration with existing AI workflows. The absence of unified protocols increases technical complexity for developers and enterprises. Industry associations and research groups are working to establish shared guidelines, but progress is gradual. Until standards mature, interoperability issues will continue to hinder the scalability of federated learning solutions.

Covid-19 Impact:

The Covid-19 pandemic accelerated the need for privacy-preserving data collaboration across industries. Healthcare institutions in particular adopted federated learning to analyze patient data without exposing sensitive information. Disruptions in global operations also increased reliance on decentralized systems that reduce data-sharing risks. Remote work environments encouraged organizations to explore distributed AI models that could function across multiple devices. The crisis highlighted the importance of secure, collaborative analytics, raising interest in federated learning research.

The solutions segment is expected to be the largest during the forecast period

The solutions segment is expected to account for the largest market share during the forecast period, driven by growing enterprise demand for ready-to-deploy platforms that simplify decentralized training. These solutions offer built-in security, model management, and orchestration capabilities. Businesses across finance, healthcare, and retail prefer comprehensive software suites over custom development. The rising need for data privacy compliance further boosts adoption of packaged federated learning solutions.

The automotive segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the automotive segment is predicted to witness the highest growth rate, due to increasing deployment of connected cars and autonomous systems are driving the need for collaborative model training. Federated learning enables automotive companies to utilize vehicle-generated data without transferring it to centralized servers. This enhances real-time decision-making while maintaining user privacy. Applications include driver behavior modeling, predictive maintenance, and advanced perception systems.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share. Strong technological infrastructure and early adoption of advanced AI frameworks support this dominance. The region's regulatory focus on data privacy encourages enterprises to adopt federated learning. Leading tech companies and research institutions continue to invest heavily in decentralized AI advancements. Industry collaborations and government-backed initiatives further accelerate market growth.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR. Rapid digitalization, expanding mobile ecosystems, and strong AI investments fuel this growth. Countries like China, Japan, South Korea, and India are actively exploring decentralized AI models for large-scale applications. Enterprises in sectors such as healthcare, retail, and manufacturing are adopting privacy-preserving technologies to handle massive datasets. Government initiatives supporting AI innovation further strengthen regional momentum.

Key players in the market

Some of the key players in Federated Learning Market include Google, Intellegent, Apple, Sherpa.ai, NVIDIA, Secure AI, Microsoft, DataFleets, IBM, Enveil, Intel, Lifebit, Cloudera, Flower, and Owkin.

Key Developments:

In November 2025, IBM and the University of Dayton announced an agreement for the joint research and development of next-generation semiconductor technologies and materials. The collaboration aims to advance critical technologies for the age of AI including AI hardware, advanced packaging, and photonics.

In November 2025, Cisco, in collaboration with Intel, has announced a first-of-its-kind integrated platform for distributed AI workloads. Powered by Intel(R) Xeon(R) 6 system-on-chip (SoC), the solution brings compute, networking, storage and security closer to data generated at the edge for real-time AI inferencing and agentic workloads.

Components Covered:

Solutions
Services

Deployment Modes Covered:

Cloud
On-Premises
Hybrid / Edge

Learning Types Covered:

Horizontal Federated Learning
Vertical Federated Learning
Federated Transfer Learning

Communication Patterns Covered:

Cross-Device Federated Learning
Cross-Silo Federated Learning

Applications Covered:

Data Privacy & Security
IoT & Edge Device Analytics
Personalized Recommendations
Autonomous Driving & Mobility
Predictive Analytics
Remote Patient Monitoring
Fraud Detection & Risk Scoring
Medical Imaging & Diagnostics

Organization Sizes Covered:

Large Enterprises
Small & Medium Enterprises (SMEs)

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 2024, 2025, 2026, 2028, and 2032
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

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 Emerging Markets
3.8 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 Market, By Component

5.1 Introduction
5.2 Solutions
5.3 Services
- 5.3.1 Consulting
- 5.3.2 Support & Maintenance
- 5.3.3 Integration & Deployment

6 Global Federated Learning Market, By Deployment Mode

6.1 Introduction
6.2 Cloud
6.3 On-Premises
6.4 Hybrid / Edge

7 Global Federated Learning Market, By Learning Type

7.1 Introduction
7.2 Horizontal Federated Learning
7.3 Vertical Federated Learning
7.4 Federated Transfer Learning

8 Global Federated Learning Market, By Communication Pattern

8.1 Introduction
8.2 Cross-Device Federated Learning
8.3 Cross-Silo Federated Learning

9 Global Federated Learning Market, By Application

9.1 Introduction
9.2 Data Privacy & Security
9.3 IoT & Edge Device Analytics
9.4 Personalized Recommendations
9.5 Autonomous Driving & Mobility
9.6 Predictive Analytics
9.7 Remote Patient Monitoring
9.8 Fraud Detection & Risk Scoring
9.9 Medical Imaging & Diagnostics

10 Global Federated Learning Market, By Organization Size

10.1 Introduction
10.2 Large Enterprises
10.3 Small & Medium Enterprises (SMEs)

11 Global Federated Learning Market, By Geography

11.1 Introduction
11.2 North America
- 11.2.1 US
- 11.2.2 Canada
- 11.2.3 Mexico
11.3 Europe
- 11.3.1 Germany
- 11.3.2 UK
- 11.3.3 Italy
- 11.3.4 France
- 11.3.5 Spain
- 11.3.6 Rest of Europe
11.4 Asia Pacific
- 11.4.1 Japan
- 11.4.2 China
- 11.4.3 India
- 11.4.4 Australia
- 11.4.5 New Zealand
- 11.4.6 South Korea
- 11.4.7 Rest of Asia Pacific
11.5 South America
- 11.5.1 Argentina
- 11.5.2 Brazil
- 11.5.3 Chile
- 11.5.4 Rest of South America
11.6 Middle East & Africa
- 11.6.1 Saudi Arabia
- 11.6.2 UAE
- 11.6.3 Qatar
- 11.6.4 South Africa
- 11.6.5 Rest of Middle East & Africa

12 Key Developments

12.1 Agreements, Partnerships, Collaborations and Joint Ventures
12.2 Acquisitions & Mergers
12.3 New Product Launch
12.4 Expansions
12.5 Other Key Strategies

13 Company Profiling

13.1 Google
13.2 Intellegens
13.3 Apple
13.4 Sherpa.ai
13.5 NVIDIA
13.6 Secure AI Labs
13.7 Microsoft
13.8 DataFleets
13.9 IBM
13.10 Enveil
13.11 Intel
13.12 Lifebit
13.13 Cloudera
13.14 Flower
13.15 Owkin

簡介目錄圖表

List of Tables

Table 1 Global Federated Learning Market Outlook, By Region (2024-2032) ($MN)
Table 2 Global Federated Learning Market Outlook, By Component (2024-2032) ($MN)
Table 3 Global Federated Learning Market Outlook, By Solutions (2024-2032) ($MN)
Table 4 Global Federated Learning Market Outlook, By Services (2024-2032) ($MN)
Table 5 Global Federated Learning Market Outlook, By Consulting (2024-2032) ($MN)
Table 6 Global Federated Learning Market Outlook, By Support & Maintenance (2024-2032) ($MN)
Table 7 Global Federated Learning Market Outlook, By Integration & Deployment (2024-2032) ($MN)
Table 8 Global Federated Learning Market Outlook, By Deployment Mode (2024-2032) ($MN)
Table 9 Global Federated Learning Market Outlook, By Cloud (2024-2032) ($MN)
Table 10 Global Federated Learning Market Outlook, By On-Premises (2024-2032) ($MN)
Table 11 Global Federated Learning Market Outlook, By Hybrid / Edge (2024-2032) ($MN)
Table 12 Global Federated Learning Market Outlook, By Learning Type (2024-2032) ($MN)
Table 13 Global Federated Learning Market Outlook, By Horizontal Federated Learning (2024-2032) ($MN)
Table 14 Global Federated Learning Market Outlook, By Vertical Federated Learning (2024-2032) ($MN)
Table 15 Global Federated Learning Market Outlook, By Federated Transfer Learning (2024-2032) ($MN)
Table 16 Global Federated Learning Market Outlook, By Communication Pattern (2024-2032) ($MN)
Table 17 Global Federated Learning Market Outlook, By Cross-Device Federated Learning (2024-2032) ($MN)
Table 18 Global Federated Learning Market Outlook, By Cross-Silo Federated Learning (2024-2032) ($MN)
Table 19 Global Federated Learning Market Outlook, By Application (2024-2032) ($MN)
Table 20 Global Federated Learning Market Outlook, By Data Privacy & Security (2024-2032) ($MN)
Table 21 Global Federated Learning Market Outlook, By IoT & Edge Device Analytics (2024-2032) ($MN)
Table 22 Global Federated Learning Market Outlook, By Personalized Recommendations (2024-2032) ($MN)
Table 23 Global Federated Learning Market Outlook, By Autonomous Driving & Mobility (2024-2032) ($MN)
Table 24 Global Federated Learning Market Outlook, By Predictive Analytics (2024-2032) ($MN)
Table 25 Global Federated Learning Market Outlook, By Remote Patient Monitoring (2024-2032) ($MN)
Table 26 Global Federated Learning Market Outlook, By Fraud Detection & Risk Scoring (2024-2032) ($MN)
Table 27 Global Federated Learning Market Outlook, By Medical Imaging & Diagnostics (2024-2032) ($MN)
Table 28 Global Federated Learning Market Outlook, By Organization Size (2024-2032) ($MN)
Table 29 Global Federated Learning Market Outlook, By Large Enterprises (2024-2032) ($MN)
Table 30 Global Federated Learning Market Outlook, By Small & Medium Enterprises (SMEs) (2024-2032) ($MN)