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市場調查報告書

聯邦學習:人工智能模型構建的新方法

Federated Learning: New Approach to Building AI Models

出版商 Frost & Sullivan 商品編碼 1024333
出版日期 內容資訊 英文 52 Pages
商品交期: 最快1-2個工作天內
價格
聯邦學習:人工智能模型構建的新方法 Federated Learning: New Approach to Building AI Models
出版日期: 2021年07月29日內容資訊: 英文 52 Pages
簡介

聯邦學習是一種分佈式機器學習架構,允許您使用分佈式數據學習全局模型,目標是準確有效地利用整個組織的數據。傳統的機器學習模型已經對海量數據進行了密集處理。但是,由於各地區迫切需要保護數據隱私並遵守嚴格的法規, "合作學習(federated learning)" 作為一種新的強大替代方案應運而生。...聯合學習允許您在本地訓練模型,而不是從中央服務器或雲檢索數據。該技術保證隱私保護並學習更好的全局模型,而無需交換包含個人或敏感信息的原始數據。

本報告探索了聯合學習市場,分析了增長和製約因素,分析了 IP 條件、關鍵推動因素和技術發展趨勢、關鍵應用領域、增長機會領域和關鍵公司。它總結了分析。

目錄

第 1 章戰略要求

  • 為什麼增長變得更加困難
  • 戰略要務 8
  • 三大戰略建議對聯邦學習行業的影響
  • 關於增長管道引擎
  • 推動增長管道引擎的增長機會

第二章增長機會分析

  • 調查範圍
  • 調查方法
  • 調查方法說明
  • 主要調查結果

第3章聯邦學習概述

  • 聯邦學習作為 ML 實施的一種新設計出現
  • 各種聯想學習類型
  • 典型的聯邦學習應用

第 4 章市場預測/驅動因素/問題

  • 市場預測和區域洞察
  • 聯邦學習市場增長因素和約束

第五章聯邦學習的主要研究方向

  • 聯邦學習調查的方向
  • 系統模型設計及應用領域相關的研究課題
  • 如何確保隱私、安全和資源管理

第6章IP態勢分析

  • 中國和美國的專利出版物數量最多
  • 微眾銀行和 IBM 在專利活動方面處於世界領先地位

第 7 章主要促成因素和最新技術發展

  • 在市場上開發聯邦學習的主要推動因素
  • 科技巨頭推動行業創新
  • 市場、智能計算服務和醫學調查是聯邦學習的新優先領域

第8章主要公司

  • Edgify Ltd.(美國)
  • Owkin Inc.(美國)
  • Fetch.ai Limited(英國)
  • Sherpa Europe S.L.(西班牙)
  • 微眾銀行股份有限公司 (中國)

第 9 章增長機會領域

  • 增長機會 1:使用開源策略構建聯邦學習生態系統
  • 增長機會 2:戰略合作,加速技術發展,滿足市場巨大需求
  • 增長機會 3:隱私保護法規的聯邦學習

第10章主要聯繫方式

第 11 章後續步驟

目錄
Product Code: DA0B

Leveraging Open Source Tools to Accelerate Technology Development across Organizations and Regions

Traditional machine learning (ML) models are centralized and involve vast amounts of data. However, both the urgency to guarantee data privacy and to abide by strict regulations imposed across regions have contributed to the emergence of a new and powerful alternative technique, federated learning. Instead of acquiring data from a central server or cloud, federated learning allows localized model training. The technique ensures privacy preservation, and better global models are trained without exchanging raw data that holds private and sensitive information. Attracted to this powerful privacy-protecting technique, a growing number of market participants, academics, and end-use industries are adopting federated learning at an unprecedented rate.

Federated learning is a distributed ML architecture that enables a global model to be trained using decentralized data. It is intended to utilize data from across an organization accurately and effectively. To help companies gain valuable insights about this emerging technique, this report offers an overview of the federated learning industry, market dynamics, key market players, research directions, key application areas, and recent developments.

The following chapters are included:

  • Overview of federated learning
  • Market forecast, drivers, and challenges
  • Key research directions for federated learning
  • IP landscape analysis
  • Key enablers and recent technology developments
  • Companies to action, including Edgify, Owkin, Fetch.ai, Sherpa Europe, and WeBank
  • Growth opportunities

Table of Contents

1.0 Strategic Imperatives

  • 1.1 Why Is It Increasingly Difficult to Grow?The Strategic Imperative 8™: Factors Creating Pressure on Growth
  • 1.2 The Strategic Imperative 8™
  • 1.3 The Impact of the Top Three Strategic Imperatives on Building AI Models for the Federated Learning Industry
  • 1.4 About the Growth Pipeline Engine™
  • 1.5 Growth Opportunities Fuel the Growth Pipeline Engine™

2.0 Growth Opportunity Analysis

  • 2.1 Research Scope
  • 2.2 Research Methodology
  • 2.3 Research Methodology Explained
  • 2.4 Key Findings

3.0 Overview of Federated Learning

  • 3.1 Federated Learning Emerging as New Design for ML Implementation
  • 3.2 Different Federated Learning Types
  • 3.3 Typical Federated Learning Applications

4.0 Market Forecast, Key Drivers, and Challenges

  • 4.1 Federated Learning Market Forecast and Regional Insights
  • 4.2 Growth Drivers and Restraints of Federated Learning Market

5.0 Key Research Directions for Federated Learning

  • 5.1 Research Directions for Federated Learning
  • 5.2 Various Research Topics Related to System Model Design and Application Areas
  • 5.3 Methods to Ensure Privacy, Security, and Resource Management

6.0 IP Landscape Analysis

  • 6.1 China and the US Demonstrate the Most Patent Publications
  • 6.2 WeBank and IBM Lead Patenting Activities across the Globe

7.0 Key Enablers and Recent Technology Developments

  • 7.1 Key Enablers of Developing Federated Learning in the Market
  • 7.2 Technology Giants Driving Industry Innovation
  • 7.3 Marketplace, Intelligent Computing Service, and Medical Research as Emerging Focus Areas for Federated Learning

8.0 Companies to Action

  • 8.1 Edgify Ltd., UK
  • 8.2 Owkin Inc., US
  • 8.3 Fetch.ai Limited, UK
  • 8.4 Sherpa Europe S.L., Spain
  • 8.5 WeBank Co., Ltd., China

9.0 Growth Opportunity Universe

  • 9.1 Growth Opportunity 1: Open-source Strategy for Building Federated Learning Ecosystem
  • 9.1 Growth Opportunity 1: Open-source Strategy for Building Federated Learning Ecosystem (continued)
  • 9.2 Growth Opportunity 2: Strategic Partnerships for Accelerating Technology Development to Meet Substantial Market Demand
  • 9.2 Growth Opportunity 2: Strategic Partnerships for Accelerating Technology Development to Meet Substantial Market Demands (continued)
  • 9.3 Growth Opportunity 3: Federated Learning for Complying with Privacy-Preserving Regulations
  • 9.3 Growth Opportunity 3: Federated Learning for Complying with the Privacy-Preserving Regulations (continued)

10.0 Key Contacts

  • 10.1 Key Contacts

11.0 Next Steps

  • 11.1 Your Next Steps
  • 11.2 Why Frost, Why Now?
  • Legal Disclaimer