フェデレーテッドラーニング市場、2032年に3億4192万ドルへ拡大見込み：プライバシー保護型AIが急成長
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The federated learning market, valued at $127.75 million in 2023, is expected to expand to $341.92 million by 2032, registering a CAGR of 11.60% from 2024 to 2032. The market expansion is being driven by a growing emphasis on data privacy, the spread of distributed AI architecture, and the ongoing digitalization across industries.
Background to Market Growth
: Federated learning is gaining attention as a method for training models while retaining data locally on each device or facility, rather than centralizing it. Approximately 67% of companies are considering or have implemented federated learning, and in the healthcare field in particular, over 80% of organizations view it as a key technology for advancing AI analysis while protecting patient data. Large-scale trials utilizing millions to over 10 million edge devices are also underway, demonstrating its effectiveness in reducing data transfer volume by up to 90% and reducing the risk of data leaks by more than 50%.
R&D investment is also on the rise, with over $400 million expected to be spent in 2023. It has been reported that improved algorithm accuracy will result in a 30% improvement in accuracy compared to traditional intensive learning models. With the strengthening of regulations such as GDPR and CCPA, companies are likely to continue seeking safe and reliable AI operations.
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Market Segmentation Analysis:
By Application:
The Industrial IoT （IIoT） segment led the market in 2023, accounting for 25.04% of the market share. Because IIoT is inherently distributed, it is well suited to federated learning, which is highly valued for its ability to continuously improve AI models while linking a large number of devices across the board. In fact, companies using IIoT have seen operational cost savings of up to 20% through advanced data utilization. By
Company Size:
The large enterprise segment dominated the market, accounting for 62.08% of the market share in 2023. Large enterprises with multiple locations strongly favor the ability to collaboratively improve AI models without centralizing sensitive data, with adoption particularly strong in the finance, healthcare, and telecommunications sectors. The ability to rapidly build highly accurate models while leveraging diverse data sources is the primary driver of adoption. By
Region:
North America
: North America led the market in 2023, capturing a 36.08% share. As major companies in the healthcare, finance, and technology sectors actively pursue advanced AI applications, federated learning is gaining attention as a solution that can meet strict privacy requirements. Numerous pilot projects and inter-company collaborations are underway, accelerating commercialization.
Asia-Pacific:
The Asia-Pacific region is projected to grow at an annual rate of 14.6% from 2024 to 2032. China, Japan, South Korea, and Singapore are the leading countries in AI research and development, and government-led technology investments are supporting the market foundation. Rising demand for federated AI in a wide range of sectors, including healthcare, manufacturing, and finance, is driving the adoption of federated learning.
Read the full report here:
https://www.snsinsider.com/reports/federated-learning-market-3597 Competitive Landscape
: The market is diverse, with players ranging from global technology companies to specialized startups. Key players include: Companies such as
Google （TensorFlow Federated）,
Apple （Core ML）
, Microsoft （Azure ML） ,
NVIDIA （Clara FL）,
IBM （Federated Learning on Watson）, and
Amazon Web Services （SageMaker） are leveraging a variety of technologies, including federated averaging, differential privacy, SMPC, blockchain FL, and cross-device FL, to build secure, high-performance learning environments.
Recent Developments:
In September 2024, Cloudera announced new AMPs to accelerate AI adoption, strengthening its efforts to help companies accelerate their AI adoption.
In May 2022, Edge Delta, an observation platform leveraging distributed stream processing and federated machine learning, raised $63 million in Series B funding.
Related reports:
Data Mesh Market:
https://www.snsinsider.com/reports/data-mesh-market-8529 Deep Learning Market:
https://www.snsinsider.com/reports/deep-learning-market-5977配信元企業：SNS INSIDER PVT. LTD.
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記事提供：DreamNews
