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시장보고서
상품코드
2132433
미국의 연합 학습 시장 규모, 점유율, 동향 분석 : 조직 규모별, 업종별, 용도별 및 부문별 예측(2026-2033년)U.S. Federated Learning Market Size, Share & Trends Analysis Report By Organization Size, By Industry Vertical, By Application, And Segment Forecasts, 2026 - 2033 |
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미국 페더레이티드 러닝 시장: 요약
미국의 연합 학습(페더레이티드 러닝) 시장 규모는 2025년에 3,270만 달러로 평가되며, 2026년 3,650만 달러에서 2033년까지 1억 2,780만 달러로 성장하며, 2026-2033년까지 CAGR은 19.6%에 달할 것으로 예측됩니다.
기업이 분산형 디바이스 전반에 AI 모델을 도입하는 움직임이 가속화되는 가운데, 페더레이티드 러닝을 안전한 집계, 차등 프라이버시, 엣지 MLOps와 결합함으로써 기밀 데이터를 중앙 집중화하지 않고도 데이터 보안, 모델 거버넌스 및 실시간 AI 성능 향상을 도모하고 있으며, 시장이 성장하고 있습니다.
데이터 프라이버시에 대한 관심이 높아지는 것이 시장 촉진요인으로 작용하고 있습니다. 의료·생명과학, 은행, 보험, 정부 기관 등의 조직은 기밀 데이터의 저장 및 공유와 관련된 엄격한 규제를 준수해야 합니다. 페더레이티드 러닝을 통해 로컬 디바이스나 서버에서 원시 데이터를 전송하지 않고도 AI 모델 학습이 가능해지므로, 조직은 규제 요건을 준수하면서 데이터 보안을 유지할 수 있습니다. 이러한 접근 방식은 데이터 유출 위험을 줄여주며, 개인정보 보호가 중요한 관심사인 산업 분야에서 AI 도입을 촉진합니다. 그 결과, 기업은 안전하고 규제를 준수하는 AI 애플리케이션을 개발하기 위해 페더레이티드 러닝 솔루션에 대한 투자를 확대하고 있습니다.
의료·생명과학, BFSI(은행·금융 서비스·보험), 제조, 통신 등의 산업 분야에서 인공지능의 급속한 보급이 미국내 페더레이티드 러닝 수요를 견인하고 있습니다. 조직에서는 질병 진단, 부정 행위 탐지, 예측 유지보수, 고객 서비스 등의 용도로 AI를 활용하는 사례가 증가하고 있습니다. 귀중한 데이터는 대개 여러 곳에 분산되어 저장되므로 페더레이티드 러닝을 통해 조직은 데이터세트를 중앙 집중화하지 않고도 공동으로 AI 모델을 학습시킬 수 있습니다. 이를 통해 기밀성이 높은 비즈니스 정보와 고객 정보를 보호하면서 모델의 정확도를 향상시킬 수 있습니다. 따라서 기업 전반에 걸쳐 AI 기반 솔루션 도입이 확대되면서, 페더레이티드 러닝 기술에 대한 강력한 수요가 창출되고 있습니다.
엣지 컴퓨팅 인프라의 확대와 연결 기기의 증가가 시장 성장에 크게 기여하고 있습니다. 스마트폰, IoT 기기, 자율주행차, 웨어러블 기기, 산업용 센서는 네트워크의 엣지에서 끊임없이 대량의 데이터를 생성하고 있습니다. 페더레이티드 러닝을 통해 AI 모델을 이러한 기기에서 직접 학습시킬 수 있게 되어, 대규모 데이터세트를 중앙 집중화된 클라우드 플랫폼으로 전송할 필요성이 줄어듭니다. 이를 통해 응답 시간이 단축되고, 네트워크 대역폭 요구 사항이 감소하며, 데이터 보안이 강화됩니다. 기업이 엣지 AI 및 분산 컴퓨팅 환경에 대한 투자를 지속함에 따라 페더레이티드 러닝의 도입은 꾸준히 증가할 것으로 예상됩니다.
자주 묻는 질문
목차
제1장 분석 방법·범위
제2장 개요
제3장 미국의 연합 학습 시장 : 변동 요인·동향·범위
제4장 미국의 연합 학습 시장 : 조직 규모별 추정·동향 분석
제5장 미국의 연합 학습 시장 : 용도별 추정·동향 분석
제6장 미국의 연합 학습 시장 : 업종별 추정·동향 분석
제7장 미국의 연합 학습 시장 : 국가별 추정·동향 분석
제8장 경쟁 구도
KSA 26.09.30U.S. Federated Learning Market Summary
The U.S. federated learning market size was valued at USD 32.7 million in 2025 and is projected to grow from USD 36.5 million in 2026 to USD 127.8 million by 2033, at a CAGR of 19.6% from 2026 to 2033. The market is growing as enterprises increasingly deploy AI models across distributed devices while combining federated learning with secure aggregation, differential privacy, and edge MLOps to improve data security, model governance, and real-time AI performance without centralizing sensitive data.
The growing emphasis on data privacy is a major market driver. Organizations across healthcare & life sciences, banking, insurance, and government sectors are required to comply with strict regulations regarding the storage and sharing of sensitive data. Federated learning enables AI models to be trained without transferring raw data from local devices or servers, helping organizations maintain data security while complying with regulatory requirements. This approach reduces the risk of data breaches and supports the adoption of AI in industries where privacy is a key concern. As a result, enterprises are increasingly investing in federated learning solutions to develop secure and compliant AI applications.
The rapid adoption of artificial intelligence across industries such as Healthcare & Life Sciences, BFSI, manufacturing, and telecommunications is driving demand for federated learning in the U.S. Organizations are increasingly using AI for applications including disease diagnosis, fraud detection, predictive maintenance, and customer service. Since valuable data is often stored across multiple locations, federated learning enables organizations to collaboratively train AI models without centralizing their datasets. This improves model accuracy while protecting sensitive business and customer information. The growing deployment of AI-powered solutions across enterprises is therefore creating strong demand for federated learning technologies.
The expansion of edge computing infrastructure and the increasing number of connected devices are significantly contributing to market growth. Smartphones, IoT devices, autonomous vehicles, wearable devices, and industrial sensors continuously generate large volumes of data at the network edge. Federated learning allows AI models to be trained directly on these devices, reducing the need to transfer large datasets to centralized cloud platforms. This improves response times, lowers network bandwidth requirements, and enhances data security. As businesses continue investing in edge AI and distributed computing environments, the adoption of federated learning is expected to increase steadily.
U.S. Federated Learning Market Report Segmentation
This report forecasts revenue growth at the country level and provides an analysis of the latest industry trends in each of the sub-segments from 2021 to 2033. For this study, Grand View Research has segmented the U.S. federated learning market report based on organization size, application, and industry vertical:
- Organization Size Outlook (Revenue, USD Million, 2021 - 2033)
- SMEs
- Large Enterprises
- Application Outlook (Revenue, USD Million, 2021 - 2033)
- Drug Discovery
- Industrial internet of things
- Risk Management
- Augmented and Virtual Reality
- Data Privacy Management
- Others
- Industry Vertical Outlook (Revenue, USD Million, 2021 - 2033)
- BFSI
- Retail & E-commerce
- IT & Telecommunication
- Healthcare & Life Sciences
- Automotive
- Others
Table of Contents
Chapter 1. Methodology and Scope
- 1.1. Market Segmentation and Scope
- 1.2. Market Definitions
- 1.3. Research Methodology
- 1.3.1. Information Procurement
- 1.3.2. Information or Data Analysis
- 1.3.3. Market Formulation & Data Visualization
- 1.3.4. Data Validation & Publishing
- 1.4. Research Scope and Assumptions
- 1.4.1. List of Data Sources
Chapter 2. Executive Summary
- 2.1. Market Outlook
- 2.2. Segment Outlook
- 2.3. Competitive Insights
Chapter 3. U.S. Federated Learning Market Variables, Trends, & Scope
- 3.1. Market Introduction/Lineage Outlook
- 3.2. Market Value Chain Analysis
- 3.3. Market Dynamics
- 3.3.1. Market Drivers Analysis
- 3.3.2. Market Restraints Analysis
- 3.3.3. Industry Opportunities
- 3.3.4. Industry Challenges
- 3.4. U.S. Federated Learning Market Analysis Tools
- 3.4.1. Porter's Analysis
- 3.4.1.1. Bargaining power of the suppliers
- 3.4.1.2. Bargaining power of the buyers
- 3.4.1.3. Threats of substitution
- 3.4.1.4. Threats from new entrants
- 3.4.1.5. Competitive rivalry
- 3.4.2. PESTEL Analysis
- 3.4.2.1. Political landscape
- 3.4.2.2. Economic and Social landscape
- 3.4.2.3. Technological landscape
- 3.4.2.4. Environmental landscape
- 3.4.2.5. Legal landscape
- 3.4.1. Porter's Analysis
Chapter 4. U.S. Federated Learning Market: Organization Size Estimates & Trend Analysis
- 4.1. Segment Dashboard
- 4.2. U.S. Federated Learning Market: Organization Size Movement Analysis, USD Million, 2025 & 2033
- 4.3. SMEs
- 4.3.1. SMEs Market Revenue Estimates and Forecasts, 2021 - 2033 (USD Million)
- 4.4. Large Enterprises
- 4.4.1. Large Enterprises Market Revenue Estimates and Forecasts, 2021 - 2033 (USD Million)
Chapter 5. U.S. Federated Learning Market: Application Estimates & Trend Analysis
- 5.1. Segment Dashboard
- 5.2. U.S. Federated Learning Market: Application Movement Analysis, USD Million, 2025 & 2033
- 5.3. Drug Discovery
- 5.3.1. Drug Discovery Market Revenue Estimates and Forecasts, 2021 - 2033 (USD Million)
- 5.4. Industrial internet of things
- 5.4.1. Industrial internet of things Market Revenue Estimates and Forecasts, 2021 - 2033 (USD Million)
- 5.5. Risk Management
- 5.5.1. Risk Management Market Revenue Estimates and Forecasts, 2021 - 2033 (USD Million)
- 5.6. Augmented and Virtual Reality
- 5.6.1. Augmented and Virtual Reality Market Revenue Estimates and Forecasts, 2021 - 2033 (USD Million)
- 5.7. Data Privacy Management
- 5.7.1. Data Privacy Management Revenue Estimates and Forecasts, 2021 - 2033 (USD Million)
- 5.8. Others
- 5.8.1. Others Market Revenue Estimates and Forecasts, 2021 - 2033 (USD Million)
Chapter 6. U.S. Federated Learning Market: Industry Vertical Estimates & Trend Analysis
- 6.1. Segment Dashboard
- 6.2. U.S. Federated Learning Market: Industry Vertical Movement Analysis, USD Million, 2025 & 2033
- 6.3. BFSI
- 6.3.1. BFSI Market Revenue Estimates and Forecasts, 2021 - 2033 (USD Million)
- 6.4. Retail & E-commerce
- 6.4.1. Retail & E-commerce Market Revenue Estimates and Forecasts, 2021 - 2033 (USD Million)
- 6.5. IT & Telecommunication
- 6.5.1. IT & Telecommunication Market Revenue Estimates and Forecasts, 2021 - 2033 (USD Million)
- 6.6. Healthcare & Life Sciences
- 6.6.1. Healthcare & Life Sciences Market Revenue Estimates and Forecasts, 2021 - 2033 (USD Million)
- 6.7. Automotive
- 6.7.1. Automotive Market Revenue Estimates and Forecasts, 2021 - 2033 (USD Million)
- 6.8. Others
- 6.8.1. Others Market Revenue Estimates and Forecasts, 2021 - 2033 (USD Million)
Chapter 7. U.S. Federated Learning Market: Country Estimates & Trend Analysis
- 7.1. U.S. Federated Learning Market Share, By Country, 2025 & 2033 USD Million
- 7.1.1. U.S. Federated Learning Market Estimates and Forecasts, 2021 - 2033 (USD Million)
- 7.1.2. U.S. Federated Learning Market Estimates and Forecasts, by Organization Size, 2021 - 2033 (USD Million)
- 7.1.3. U.S. Federated Learning Market Estimates and Forecasts, by Application, 2021 - 2033 (USD Million)
- 7.1.4. U.S. Federated Learning Market Estimates and Forecasts, by Industry Vertical, 2021 - 2033 (USD Million)
Chapter 8. Competitive Landscape
- 8.1. Recent Developments & Impact Analysis by Key Market Participants
- 8.2. Company Categorization
- 8.3. Company Market Positioning, 2025
- 8.4. Company Market Share Analysis, 2025
- 8.5. Company Heat Map Analysis
- 8.6. Strategy Mapping
- 8.7. Company Profiles
- 8.7.1. Google LLC
- 8.7.1.1. Participant's Overview
- 8.7.1.2. Financial Performance
- 8.7.1.3. Product Benchmarking
- 8.7.1.4. Recent Developments
- 8.7.2. IBM Corporation
- 8.7.2.1. Participant's Overview
- 8.7.2.2. Financial Performance
- 8.7.2.3. Product Benchmarking
- 8.7.2.4. Recent Developments
- 8.7.3. NVIDIA Corporation
- 8.7.3.1. Participant's Overview
- 8.7.3.2. Financial Performance
- 8.7.3.3. Product Benchmarking
- 8.7.3.4. Recent Developments
- 8.7.4. Intel Corporation
- 8.7.4.1. Participant's Overview
- 8.7.4.2. Financial Performance
- 8.7.4.3. Product Benchmarking
- 8.7.4.4. Recent Developments
- 8.7.5. FedML Inc.
- 8.7.5.1. Participant's Overview
- 8.7.5.2. Financial Performance
- 8.7.5.3. Product Benchmarking
- 8.7.5.4. Recent Developments
- 8.7.6. Owkin Inc.
- 8.7.6.1. Participant's Overview
- 8.7.6.2. Financial Performance
- 8.7.6.3. Product Benchmarking
- 8.7.6.4. Recent Developments
- 8.7.7. Acuratio Inc.
- 8.7.7.1. Participant's Overview
- 8.7.7.2. Financial Performance
- 8.7.7.3. Product Benchmarking
- 8.7.7.4. Recent Developments
- 8.7.8. Cloudera Inc.
- 8.7.8.1. Participant's Overview
- 8.7.8.2. Financial Performance
- 8.7.8.3. Product Benchmarking
- 8.7.8.4. Recent Developments
- 8.7.9. Edge Delta Inc.
- 8.7.9.1. Participant's Overview
- 8.7.9.2. Financial Performance
- 8.7.9.3. Product Benchmarking
- 8.7.9.4. Recent Developments
- 8.7.10. Enveil Inc.
- 8.7.10.1. Participant's Overview
- 8.7.10.2. Financial Performance
- 8.7.10.3. Product Benchmarking
- 8.7.10.4. Recent Developments
- 8.7.1. Google LLC
