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1857059

全球聯邦學習和同態加密市場：未來預測（至 2032 年）—按組件、部署方法、技術、應用、最終用戶和地區進行分析

Federated Learning & Homomorphic Encryption Market Forecasts to 2032 - Global Analysis By Component, Deployment Mode, Technology, Application, End User and By Geography

出版日期: 2025年10月01日 | 出版商:

Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3個工作天內

價格

簡介目錄圖表

根據 Stratistics MRC 的數據，全球聯邦學習和同態密碼市場預計到 2025 年將達到 7.86 億美元，到 2032 年將達到 30.371 億美元，預測期內複合年成長率為 21.3%。

聯邦學習是一種分散式機器學習方法，它允許在多個設備和伺服器上進行模型訓練，而無需共用原始數據，從而保護隱私並降低數據傳輸風險。同態加密是一種密碼學技術，它允許在不解密的情況下對加密資料執行計算，從而確保資料在處理過程中的機密性。結合這些技術，可以在分散式系統中實現協同學習和分析，同時保持資料完整性並符合嚴格的資料保護條例，從而支援安全、保護隱私的人工智慧。

日益嚴格的資料隱私法規和加密技術的進步

聯邦學習無需暴露原始資料即可實現分散式學習，而同態加密則可在加密資料集上進行安全計算。這些技術在醫療、金融和國防等領域正日益普及，因為在這些領域，資料保密至關重要。同時，基於格的密碼學和安全聚合通訊協定的突破性進展，也使這些解決方案更具可擴展性。監管壓力與技術創新的融合，正推動著市場的快速擴張。

聯邦學習框架和密碼庫之間缺乏統一通訊協定

組織機構在整合各種加密方案、模型格式和通訊協定面臨許多挑戰，尤其是在多方環境中。這種碎片化增加了部署的複雜性，並限制了跨部門的可擴展性。此外，由於缺乏對性能基準和隱私保障的共識，跨行業合作也受到阻礙。缺乏統一的標準，技術孤島和整合開銷仍然限制技術的廣泛應用。

將區塊鏈與零知識證明結合

區塊鏈確保模型更新的防篡改性和去中心化的信任，而零知識證明則允許在不洩漏底層資料的情況下檢驗計算結果。這種整合在金融服務、醫療保健和政府應用領域尤其重要，因為這些領域必須兼顧透明度和隱私性。新興企業和研發機構正在積極開發將密碼學學習與分散式帳本結合的混合架構。這種融合有望重新定義人工智慧生態系統中的信任。

儘管技術已經成熟，但商業性應用進展緩慢。

企業指出，高昂的實施成本、熟練人才短缺以及投資報酬率的不確定性是主要阻礙因素。此外，在異質設備和網路中部署加密模型的複雜性也延緩了商業化進程。在對延遲和吞吐量要求嚴格的領域，效能權衡進一步阻礙了整合。如果沒有明確的商業案例和簡化的部署框架，市場成長很可能落後於技術進步。

新冠疫情的影響：

新冠疫情凸顯了安全、去中心化資料協作的必要性，尤其是在醫療保健和公共衛生分析領域。聯邦學習使醫院和研究機構能夠在不集中儲存敏感患者資料的情況下訓練模型，從而幫助疫情應對工作。然而，供應鏈中斷和預算重新分配暫時延緩了對隱私保護型人工智慧基礎設施的投資。此次危機也加速了數位轉型，促使各國政府和企業探索利用加密分析進行遠距離診斷和接觸者追蹤。

預計在預測期內，軟體框架細分市場將是最大的細分市場。

由於軟體框架在聯邦學習和加密計算中發揮基礎性作用，預計在預測期內，軟體框架領域將佔據最大的市場佔有率。這些平台提供用於模型編配、安全聚合以及跨分散式節點實作通訊協定的工具。 TensorFlow Federated 和 PySyft 等開放原始碼計劃正在推動創新，而企業級解決方案則提供可擴展性和合規性。該領域受益於持續更新、社群支援以及與雲端原生環境的整合。

預計在預測期內，SMPC細分市場將實現最高的複合年成長率。

預計在預測期內，安全多方運算 (SMPC) 領域將實現最高成長率，這主要得益於其能夠在不洩露單一輸入的情況下執行協作運算的能力。 SMPC 在金融服務、基因組學和跨境分析等領域正日益受到重視，這些領域對資料保密性要求極高。通訊協定效率和硬體加速的最新進展使 SMPC 更易於實際應用。此外，密碼學家和企業人工智慧團隊之間的合作也為該領域帶來了積極影響。

比最大的地區

預計在預測期內，北美將佔據最大的市場佔有率，這主要得益於其健全的監管框架、先進的人工智慧基礎設施以及高額的研發投入。該地區匯聚了聯邦學習和密碼學領域的主要企業，包括Google、微軟、IBM 和 Duality Technologies。政府在醫療保健、國防和金融領域推廣隱私保護技術的舉措，進一步推動了這些技術的應用。學術機構和新興企業也透過開放原始碼貢獻和試點部署，為技術創新做出貢獻。

複合年成長率最高的地區：

在預測期內，由於對安全人工智慧和密碼學研究的大力投資，北美預計將呈現最高的複合年成長率。該地區充滿活力的新興企業生態系統正在推動跨學科聯邦學習和同態加密的商業化。聯邦政府對隱私保護技術和人工智慧倫理的資助正在加速創新。學術界、產業界和政府之間的戰略夥伴關係正在促進可擴展的部署。

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
- 其他南美洲
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11章：主要趨勢

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

第12章：公司簡介

Google
Microsoft
IBM
Intel
NVIDIA
Amazon Web Services（AWS）
Meta
Apple
Qualcomm
Huawei
Baidu
Tencent
Cisco Systems
Palantir Technologies
Duality Technologies
Zama
Inpher
OpenMined
Partisia
Enveil

簡介目錄圖表

Product Code: SMRC31924

According to Stratistics MRC, the Global Federated Learning & Homomorphic Encryption Market is accounted for $786.0 million in 2025 and is expected to reach $3,037.1 million by 2032 growing at a CAGR of 21.3% during the forecast period. Federated learning is a decentralized machine learning approach that enables model training across multiple devices or servers without sharing raw data, preserving privacy and reducing data transfer risks. Homomorphic encryption is a cryptographic technique that allows computations on encrypted data without decryption, ensuring data confidentiality during processing. Together, they support secure, privacy-preserving AI by enabling collaborative learning and analytics across distributed systems while maintaining data integrity and compliance with stringent data protection regulations.

Market Dynamics:

Driver:

Rising data privacy regulations & advancements in cryptographic techniques

Federated learning enables decentralized training without exposing raw data, while homomorphic encryption allows secure computation on encrypted datasets. These technologies are gaining traction in healthcare, finance, and defense, where data sensitivity is paramount. Simultaneously, breakthroughs in lattice-based cryptography and secure aggregation protocols are making these solutions more scalable. The convergence of regulatory pressure and technical innovation is fueling rapid market expansion.

Restraint:

Lack of unified protocols across federated learning frameworks and encryption libraries

Organizations struggle to integrate diverse encryption schemes, model formats, and communication protocols, especially in multi-party environments. This fragmentation increases deployment complexity and limits scalability across sectors. Additionally, the lack of consensus on performance benchmarks and privacy guarantees hinders cross-industry collaboration. Without harmonized standards, widespread adoption remains constrained by technical silos and integration overhead.

Opportunity:

Integration with blockchain and zero-knowledge proofs

Blockchain ensures tamper-proof model updates and decentralized trust, while ZKPs allow verification of computations without revealing underlying data. These integrations are particularly valuable in financial services, healthcare, and government applications where transparency and privacy must coexist. Startups and research labs are actively developing hybrid architectures that combine encrypted learning with distributed ledgers. This convergence is expected to redefine trust in collaborative AI ecosystems.

Threat:

Slow commercial adoption despite technical maturity

Organizations cite high implementation costs, lack of skilled personnel, and uncertain ROI as key deterrents. Moreover, the complexity of deploying encrypted models across heterogeneous devices and networks slows down commercialization. In sectors with strict latency and throughput requirements, performance trade-offs further delay integration. Without clear business cases and streamlined deployment frameworks, market growth may lag behind technical progress.

Covid-19 Impact:

The COVID-19 pandemic highlighted the need for secure, decentralized data collaboration, especially in healthcare and public health analytics. Federated learning enabled hospitals and research institutions to train models on sensitive patient data without centralizing it, supporting pandemic response efforts. However, supply chain disruptions and budget reallocations temporarily slowed infrastructure investments in privacy-preserving AI. The crisis also accelerated digital transformation, prompting governments and enterprises to explore encrypted analytics for remote diagnostics and contact tracing.

The software frameworks segment is expected to be the largest during the forecast period

The software frameworks segment is expected to account for the largest market share during the forecast period due to their foundational role in enabling federated learning and encrypted computation. These platforms provide the tools for model orchestration, secure aggregation, and protocol implementation across distributed nodes. Open-source projects like TensorFlow Federated and PySyft are driving innovation, while enterprise-grade solutions offer scalability and compliance features. The segment benefits from continuous updates, community support, and integration with cloud-native environments.

The secure multi-party computation (SMPC) segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the secure multi-party computation (SMPC) segment is predicted to witness the highest growth rate driven by its ability to perform joint computations without revealing individual inputs. SMPC is gaining traction in financial services, genomics, and cross-border analytics where data confidentiality is critical. Recent advances in protocol efficiency and hardware acceleration are making SMPC more practical for real-world use. The segment is also benefiting from collaborations between cryptography researchers and enterprise AI teams.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share propelled by strong regulatory frameworks, advanced AI infrastructure, and high R&D investment. The region hosts major players in federated learning and encryption, including Google, Microsoft, IBM, and Duality Technologies. Government initiatives promoting privacy-preserving technologies in healthcare, defense, and finance are further boosting adoption. Academic institutions and startups are also contributing to innovation through open-source contributions and pilot deployments.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR due to aggressive investments in secure AI and cryptographic research. The region's dynamic startup ecosystem is driving commercialization of federated learning and homomorphic encryption across verticals. Federal funding for privacy-preserving technologies and AI ethics is accelerating innovation. Strategic partnerships between academia, industry, and government are fostering scalable deployments.

Key players in the market

Some of the key players in Federated Learning & Homomorphic Encryption Market include Google, Microsoft, IBM, Intel, NVIDIA, Amazon Web Services (AWS), Meta, Apple, Qualcomm, Huawei, Baidu, Tencent, Cisco Systems, Palantir Technologies, Duality Technologies, Zama, Inpher, OpenMined, Partisia, and Enveil

Key Developments:

In October 2025, Microsoft launched a major Copilot update featuring group chats, memory, and Mico avatar. The release emphasizes human-centered AI and deeper personalization across work and life. It includes connectors for Google services and health/education tools.

In October 2025, IBM introduced the Spyre Accelerator for scaling generative and agentic AI workloads. It will be available across IBM Z, LinuxONE, and Power systems. The launch supports enterprise-grade AI orchestration and automation.

In October 2025, Intel partnered with global retailers to launch AI-powered experience stores for the holidays. The initiative showcases hybrid AI models and personalized computing. It aims to boost consumer engagement and brand visibility.

Components Covered:

Software Frameworks
Encryption Tools
Model Aggregation Servers
Data Management Systems
Communication Protocols
Other Components

Deployment Modes Covered:

On-Premises
Cloud-Based
Hybrid Deployment

Technologies Covered:

Federated Learning
Homomorphic Encryption
Secure Multi-Party Computation (SMPC)
Differential Privacy
Blockchain Integration
Other Technologies

Applications Covered:

Healthcare Data Sharing
Financial Fraud Detection
IoT Device Security
Smart Manufacturing
Autonomous Vehicles
Predictive Maintenance
Other Applications

End Users Covered:

Healthcare & Life Sciences
Banking, Financial Services & Insurance (BFSI)
Information Technology & Telecommunications
Manufacturing
Energy & Utilities
Government & Defense
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 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 Technology Analysis
3.7 Application Analysis
3.8 End User Analysis
3.9 Emerging Markets
3.10 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 & Homomorphic Encryption Market, By Component

5.1 Introduction
5.2 Software Frameworks
5.3 Encryption Tools
5.4 Model Aggregation Servers
5.5 Data Management Systems
5.6 Communication Protocols
5.7 Other Components

6 Global Federated Learning & Homomorphic Encryption Market, By Deployment Mode

6.1 Introduction
6.2 On-Premises
6.3 Cloud-Based
6.4 Hybrid Deployment

7 Global Federated Learning & Homomorphic Encryption Market, By Technology

7.1 Introduction
7.2 Federated Learning
7.3 Homomorphic Encryption
7.4 Secure Multi-Party Computation (SMPC)
7.5 Differential Privacy
7.6 Blockchain Integration
7.7 Other Technologies

8 Global Federated Learning & Homomorphic Encryption Market, By Application

8.1 Introduction
8.2 Healthcare Data Sharing
8.3 Financial Fraud Detection
8.4 IoT Device Security
8.5 Smart Manufacturing
8.6 Autonomous Vehicles
8.7 Predictive Maintenance
8.8 Other Applications

9 Global Federated Learning & Homomorphic Encryption Market, By End User

9.1 Introduction
9.2 Healthcare & Life Sciences
9.3 Banking, Financial Services & Insurance (BFSI)
9.4 Information Technology & Telecommunications
9.5 Manufacturing
9.6 Energy & Utilities
9.7 Government & Defense
9.8 Other End Users

10 Global Federated Learning & Homomorphic Encryption Market, By Geography

10.1 Introduction
10.2 North America
- 10.2.1 US
- 10.2.2 Canada
- 10.2.3 Mexico
10.3 Europe
- 10.3.1 Germany
- 10.3.2 UK
- 10.3.3 Italy
- 10.3.4 France
- 10.3.5 Spain
- 10.3.6 Rest of Europe
10.4 Asia Pacific
- 10.4.1 Japan
- 10.4.2 China
- 10.4.3 India
- 10.4.4 Australia
- 10.4.5 New Zealand
- 10.4.6 South Korea
- 10.4.7 Rest of Asia Pacific
10.5 South America
- 10.5.1 Argentina
- 10.5.2 Brazil
- 10.5.3 Chile
- 10.5.4 Rest of South America
10.6 Middle East & Africa
- 10.6.1 Saudi Arabia
- 10.6.2 UAE
- 10.6.3 Qatar
- 10.6.4 South Africa
- 10.6.5 Rest of Middle East & Africa

11 Key Developments

11.1 Agreements, Partnerships, Collaborations and Joint Ventures
11.2 Acquisitions & Mergers
11.3 New Product Launch
11.4 Expansions
11.5 Other Key Strategies

12 Company Profiling

12.1 Google
12.2 Microsoft
12.3 IBM
12.4 Intel
12.5 NVIDIA
12.6 Amazon Web Services (AWS)
12.7 Meta
12.8 Apple
12.9 Qualcomm
12.10 Huawei
12.11 Baidu
12.12 Tencent
12.13 Cisco Systems
12.14 Palantir Technologies
12.15 Duality Technologies
12.16 Zama
12.17 Inpher
12.18 OpenMined
12.19 Partisia
12.20 Enveil

簡介目錄圖表

List of Tables

Table 1 Global Federated Learning & Homomorphic Encryption Market Outlook, By Region (2024-2032) ($MN)
Table 2 Global Federated Learning & Homomorphic Encryption Market Outlook, By Component (2024-2032) ($MN)
Table 3 Global Federated Learning & Homomorphic Encryption Market Outlook, By Software Frameworks (2024-2032) ($MN)
Table 4 Global Federated Learning & Homomorphic Encryption Market Outlook, By Encryption Tools (2024-2032) ($MN)
Table 5 Global Federated Learning & Homomorphic Encryption Market Outlook, By Model Aggregation Servers (2024-2032) ($MN)
Table 6 Global Federated Learning & Homomorphic Encryption Market Outlook, By Data Management Systems (2024-2032) ($MN)
Table 7 Global Federated Learning & Homomorphic Encryption Market Outlook, By Communication Protocols (2024-2032) ($MN)
Table 8 Global Federated Learning & Homomorphic Encryption Market Outlook, By Other Components (2024-2032) ($MN)
Table 9 Global Federated Learning & Homomorphic Encryption Market Outlook, By Deployment Mode (2024-2032) ($MN)
Table 10 Global Federated Learning & Homomorphic Encryption Market Outlook, By On-Premises (2024-2032) ($MN)
Table 11 Global Federated Learning & Homomorphic Encryption Market Outlook, By Cloud-Based (2024-2032) ($MN)
Table 12 Global Federated Learning & Homomorphic Encryption Market Outlook, By Hybrid Deployment (2024-2032) ($MN)
Table 13 Global Federated Learning & Homomorphic Encryption Market Outlook, By Technology (2024-2032) ($MN)
Table 14 Global Federated Learning & Homomorphic Encryption Market Outlook, By Federated Learning (2024-2032) ($MN)
Table 15 Global Federated Learning & Homomorphic Encryption Market Outlook, By Homomorphic Encryption (2024-2032) ($MN)
Table 16 Global Federated Learning & Homomorphic Encryption Market Outlook, By Secure Multi-Party Computation (SMPC) (2024-2032) ($MN)
Table 17 Global Federated Learning & Homomorphic Encryption Market Outlook, By Differential Privacy (2024-2032) ($MN)
Table 18 Global Federated Learning & Homomorphic Encryption Market Outlook, By Blockchain Integration (2024-2032) ($MN)
Table 19 Global Federated Learning & Homomorphic Encryption Market Outlook, By Other Technologies (2024-2032) ($MN)
Table 20 Global Federated Learning & Homomorphic Encryption Market Outlook, By Application (2024-2032) ($MN)
Table 21 Global Federated Learning & Homomorphic Encryption Market Outlook, By Healthcare Data Sharing (2024-2032) ($MN)
Table 22 Global Federated Learning & Homomorphic Encryption Market Outlook, By Financial Fraud Detection (2024-2032) ($MN)
Table 23 Global Federated Learning & Homomorphic Encryption Market Outlook, By IoT Device Security (2024-2032) ($MN)
Table 24 Global Federated Learning & Homomorphic Encryption Market Outlook, By Smart Manufacturing (2024-2032) ($MN)
Table 25 Global Federated Learning & Homomorphic Encryption Market Outlook, By Autonomous Vehicles (2024-2032) ($MN)
Table 26 Global Federated Learning & Homomorphic Encryption Market Outlook, By Predictive Maintenance (2024-2032) ($MN)
Table 27 Global Federated Learning & Homomorphic Encryption Market Outlook, By Other Applications (2024-2032) ($MN)
Table 28 Global Federated Learning & Homomorphic Encryption Market Outlook, By End User (2024-2032) ($MN)
Table 29 Global Federated Learning & Homomorphic Encryption Market Outlook, By Healthcare & Life Sciences (2024-2032) ($MN)
Table 30 Global Federated Learning & Homomorphic Encryption Market Outlook, By Banking, Financial Services & Insurance (BFSI) (2024-2032) ($MN)
Table 31 Global Federated Learning & Homomorphic Encryption Market Outlook, By Information Technology & Telecommunications (2024-2032) ($MN)
Table 32 Global Federated Learning & Homomorphic Encryption Market Outlook, By Manufacturing (2024-2032) ($MN)
Table 33 Global Federated Learning & Homomorphic Encryption Market Outlook, By Energy & Utilities (2024-2032) ($MN)
Table 34 Global Federated Learning & Homomorphic Encryption Market Outlook, By Government & Defense (2024-2032) ($MN)
Table 35 Global Federated Learning & Homomorphic Encryption Market Outlook, By Other End Users (2024-2032) ($MN)