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市场调查报告书
商品编码
1857059

全球联邦学习和同态加密市场：未来预测（至 2032 年）—按组件、部署方法、技术、应用、最终用户和地区进行分析

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: | 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，全球联邦学习和同态密码市场预计到 2025 年将达到 7.86 亿美元，到 2032 年将达到 30.371 亿美元，预测期内复合年增长率为 21.3%。

联邦学习是一种分散式机器学习方法，它允许在多个设备和伺服器上进行模型训练，而无需共用原始数据，从而保护隐私并降低数据传输风险。同态加密是一种密码学技术，它允许在不解密的情况下对加密资料执行计算，从而确保资料在处理过程中的机密性。结合这些技术，可以在分散式系统中实现协同学习和分析，同时保持资料完整性并符合严格的资料保护条例，从​​而支援安全、保护隐私的人工智慧。

日益严格的资料隐私法规和加密技术的进步

联邦学习无需暴露原始资料即可实现分散式学习，而同态加密则可在加密资料集上进行安全计算。这些技术在医疗、金融和国防等领域正日益普及，因为在这些领域，资料保密至关重要。同时，基于格的密码学和安全聚合通讯协定的突破性进展，也使这些解决方案更具可扩展性。监管压力与技术创新的融合，正推动着市场的快速扩张。

联邦学习框架和密码库之间缺乏统一通讯协定

组织机构在整合各种加密方案、模型格式和通讯协定面临许多挑战，尤其是在多方环境中。这种碎片化增加了部署的复杂性，并限制了跨部门的可扩展性。此外，由于缺乏对性能基准和隐私保障的共识，跨行业合作也受到阻碍。缺乏统一的标准，技术孤岛和整合开销仍然限制技术的广泛应用。

将区块链与零知识证明结合

区块链确保模型更新的防篡改性和去中心化的信任，而零知识证明则允许在不洩漏底层资料的情况下检验计算结果。这种整合在金融服务、医疗保健和政府应用领域尤其重要，因为这些领域必须兼顾透明度和隐私性。新兴企业和研发机构正在积极开发将密码学学习与分散式帐本结合的混合架构。这种融合有望重新定义人工智慧生态系统中的信任。

儘管技术已经成熟，但商业性应用进展缓慢。

企业指出，高昂的实施成本、熟练人才短缺以及投资报酬率的不确定性是主要阻碍因素。此外，在异质设备和网路中部署加密模型的复杂性也延缓了商业化进程。在对延迟和吞吐量要求严格的领域，效能权衡进一步阻碍了整合。如果没有明确的商业案例和简化的部署框架，市场成长很可能落后于技术进步。

新冠疫情的影响：

新冠疫情凸显了安全、去中心化资料协作的必要性，尤其是在医疗保健和公共卫生分析领域。联邦学习使医院和研究机构能够在不集中储存敏感患者资料的情况下训练模型，从而帮助疫情应对工作。然而，供应链中断和预算重新分配暂时延缓了对隐私保护型人工智慧基础设施的投资。此次危机也加速了数位转型，促使各国政府和企业探索利用加密分析进行远距离诊断和接触者追踪。

预计在预测期内，软体框架细分市场将是最大的细分市场。

由于软体框架在联邦学习和加密计算中发挥基础性作用，预计在预测期内，软体框架领域将占据最大的市场份额。这些平台提供用于模型编配、安全聚合以及跨分散式节点实作通讯协定的工具。 TensorFlow Federated 和 PySyft 等开放原始码计划正在推动创新，而企业级解决方案则提供可扩展性和合规性。该领域受益于持续更新、社群支援以及与云端原生环境的整合。

预计在预测期内，SMPC细分市场将实现最高的复合年增长率。

预计在预测期内，安全多方运算 (SMPC) 领域将实现最高成长率，这主要得益于其能够在不洩露单一输入的情况下执行协作运算的能力。 SMPC 在金融服务、基因组学和跨境分析等领域正日益受到重视，这些领域对资料保密性要求极高。通讯协定效率和硬体加速的最新进展使 SMPC 更易于实际应用。此外，密码学家和企业人工智慧团队之间的合作也为该领域带来了积极影响。

比最大的地区

预计在预测期内，北美将占据最大的市场份额，这主要得益于其健全的监管框架、先进的人工智慧基础设施以及高额的研发投入。该地区汇聚了联邦学习和密码学领域的主要企业，包括Google、微软、IBM 和 Duality Technologies。政府在医疗保健、国防和金融领域推广隐私保护技术的倡议，进一步推动了这些技术的应用。学术机构和新兴企业也透过开放原始码贡献和试点部署，为技术创新做出贡献。

复合年增长率最高的地区：

在预测期内，由于对安全人工智慧和密码学研究的大力投资，北美预计将呈现最高的复合年增长率。该地区充满活力的新兴企业生态系统正在推动跨学科联邦学习和同态加密的商业化。联邦政府对隐私保护技术和人工智慧伦理的资助正在加速创新。学术界、产业界和政府之间的战略伙伴关係正在促进可扩展的部署。

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  • 公司简介
    • 对最多三家其他公司进行全面分析
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  • 区域分类
    • 根据客户兴趣对主要国家进行市场估算、预测和复合年增长率分析（註：基于可行性检查）
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    • 基于产品系列、地域覆盖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 引言

  • 概述
  • 相关利益者
  • 分析范围
  • 分析方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 分析方法
  • 分析材料
    • 原始研究资料
    • 二手研究资讯来源
    • 先决条件

第三章 市场趋势分析

  • 司机
  • 抑制因素
  • 市场机会
  • 威胁
  • 技术分析
  • 应用分析
  • 终端用户分析
  • 新兴市场
  • 新冠疫情的感染疾病

第四章 波特五力分析

  • 供应商的议价能力
  • 买方议价能力
  • 替代产品的威胁
  • 新参与企业的威胁
  • 公司间的竞争

5. 全球联邦学习和同态加密市场（按组件划分）

  • 软体框架
  • 加密工具
  • 模型整合伺服器
  • 资料管理系统
  • 通讯协定
  • 其他部件

6. 全球联邦学习与同态加密市场依部署方式划分

  • 本地部署
  • 云端基础的
  • 混合部署

7. 全球联邦学习与同态加密市场（依技术划分）

  • 联邦学习
  • 同态加密
  • SMPC（安全多方运算）
  • 差分隐私
  • 区块链集成
  • 其他技术

8. 全球联邦学习和同态加密市场（按应用划分）

  • 医疗资料共用
  • 金融诈骗侦测
  • 物联网设备安全
  • 智慧製造
  • 自动驾驶汽车
  • 预测性维护
  • 其他用途

9. 全球联邦学习和同态加密市场（按最终用户划分）

  • 医学与生命科​​学
  • 银行、金融服务和保险（BFSI）
  • 资讯科技/通讯
  • 製造业
  • 能源与公用事业
  • 政府/国防
  • 其他最终用户

第十章 全球联邦学习与同态加密市场（按地区划分）

  • 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
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 亚太其他地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美洲
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十一章：主要趋势

  • 合约、商业伙伴关係和合资企业
  • 企业合併（M&A）
  • 新产品发布
  • 业务拓展
  • 其他关键策略

第十二章：公司简介

  • 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
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

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 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

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)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.