Federated Learning for Industrial IoT Market Size (2024 - 2030):

The Global Federated Learning for Industrial IoT Market was estimated at USD 144.65 Million in 2023 and is anticipated to be a value of USD 294.68 Million by 2030, growing at a CAGR of 10.7%.

Federated learning is a machine learning technique that involves training an algorithm on multiple decentralized edge devices or servers that hold local data samples without sharing them. The idea behind federated learning is to leverage the power of distributed computing while maintaining the privacy of the data that's being used to train the model. Traditional machine learning involves collecting and centralizing data in a single location for training a model. However, federated learning operates differently by storing data on individual devices such as IoT devices or smartphones. The central server manages the coordination of shared model training using these devices' data. The primary attribute of federated learning is the decentralized training process, in which the central server sends the current model version to every device, and each device uses its local data to train the model. After the completion of the training phase, the device forwards the updated model back to the server, which combines the outcomes from all the devices to create a new version of the model.

Federated Learning for Industrial IoT (IIOT) refers to a machine learning technique that allows edge devices within an IIoT network to collaborate in training a model without having to share their data with one another or with a central server. This technique involves the distribution of the training process across multiple edge devices, which collect and process data locally. Only the updates to the model are then sent to a central server for aggregation. This approach provides benefits such as enhanced data privacy and security, decreased network bandwidth requirements, and enables real-time decision-making and learning in industrial settings. The applications of federated learning for IIoT include energy management, quality control, predictive maintenance, and anomaly detection.

Global Federated Learning for Industrial IoT Market Drivers:

The increasing concerns about data privacy and stored data in decentralized devices are fuelling the growth of global federated learning for the industrial IoT market.

Federated learning addresses data privacy and security concerns in industrial IoT applications, where sensitive data is involved. This is achieved by enabling the analysis of data locally, eliminating the need to transfer it to a central location. With the shift in focus toward cloud-based work in businesses, the demand for safe and secure technologies is on the rise, augmenting the market size for federated learning for industrial IoT.

The widespread adoption of industrial IoT (IIoT) is also contributing to the growth of global federated learning for the industrial IOT market.

The use of industrial IoT is expanding quickly across several industry verticals because it helps companies enhance their operations and increase effectiveness. Federated learning has emerged as a preferred option for industrial IoT applications, as it enables data analysis without the need to transfer it to a central location. Thereby, enlarging the global market size.

Global Federated Learning for Industrial IoT Market Challenges:

The global federated learning for industrial IoT market is encountering a challenge of a shortage of skilled experts in this domain, which can affect its growth. Federated learning for industrial IoT is a new and complicated field that calls for a particular set of skills and knowledge. It involves integrating distributed computing, edge devices, and machine learning algorithms, all of which can be challenging to incorporate and manage. In addition, it necessitates knowledge of data science, cybersecurity, and edge computing, all of which are uncommon in conventional industrial settings. Organizations looking to make use of industrial IoT face a significant obstacle in the form of a lack of skilled workers on the market. Higher salaries, longer recruitment periods, and an increased level of competition for professionals have all been a direct result of the lack of skilled professionals in this field. The adoption and implementation of federated learning for industrial IoT may be slowed significantly as a result, hindering its potential to boost efficiency and operations.

Global Federated Learning for Industrial IoT Market Opportunities:

The application of federated learning in personalized medicines and drug discovery and the advancements in AI technologies present significant opportunities in the global federated learning for industrial IoT. 

Federated learning presents opportunities for businesses to analyze patient data for precision medicine and personalized healthcare. It can also help accelerate the drug discovery process by analyzing large amounts of bioscience data.

Moreover, businesses will benefit from more advanced federated learning algorithms due to the ongoing progress in AI technologies. This will enable them to extract more value from their data and improve their decision-making.

COVID-19 Impact on the Global Federated Learning for Industrial IoT Market:

The outbreak of the COVID-19 pandemic substantially impacted the global federated learning for the industrial IoT market. The pandemic caused disruptions in supply chains and the distribution of goods and services, including the deployment of new federated learning solutions. However, the pandemic also led to increasing demand for AI-powered solutions, including federated learning, as the companies look for ways to adapt to remote work and the changing consumer behaviour by automating and optimizing their operations. This factor resulted in new prospects for the federated learning market. Thus, the global federated learning for the industrial IoT market experienced both challenges and opportunities for growth and innovation possibilities.

Global Federated Learning for Industrial IoT Market Recent Developments:

• In February 2023, FedML, a Collaborative/Federated Machine Learning and Edge AI Platform that encourages communities to build and connect AI applications anywhere, at any scale, announced that it will collaborate with Theta Network to enable collaborative machine learning for Generative AI as well as content recommendation and advertisement, powered by the Theta edge network.
• In December 2022, Intel Labs and the Perelman School of Medicine at the University of Pennsylvania (Penn Medicine) completed a joint research study using federated learning, a distributed artificial intelligence (AI) and machine learning (ML) approach, to assist international healthcare and research institutions in identifying malignant brain tumors. The study demonstrated the ability to improve brain tumor detection by 33%, making it the largest medical federated learning study to date. They analyzed an unprecedented global dataset from 71 institutions across six continents.
• In May 2022, Microsoft AI Team launched Federated Learning Utilities and Tools for Experimentation (FLUTE), a high-performance open-source platform for offline simulations and research on federated learning.

FEDERATED LEARNING FOR INDUSTRIAL IOT MARKET REPORT COVERAGE:

Global Federated Learning for Industrial IoT Market Segmentation:

Global Federated Learning for Industrial IoT Market Segmentation: By Verticals 

• Banking, Financial Services, & Insurance
• Healthcare & Life Sciences
• Retail & E-commerce
• Manufacturing
• Energy & Utilities
• Automotive & Transportation
• IT & Telecommunication
• Others

Based on the verticals, the global federated learning for the industrial IoT market is segmented into banking, financial services, and insurance, healthcare and life sciences, retail and e-commerce, manufacturing, energy and utilities, automotive and transportation, IT and telecommunication, and others. In 2022, the automotive and transportation segment held the largest market share. The technology behind autonomous vehicles is intricate. The system makes use of various technologies, including observation, forecasting, monitoring, modeling, interfaces made possible by the public cloud, and data management. In addition to making automated vehicles reliable and secure for seamless integration across the globe, as well as wirelessly analyzing information and personal confidentiality, the introduction of automated vehicles emphasized data, edge-to-edge computer technology handling, and improved machine learning algorithms. The growth of federated learning in the vertical is anticipated to be driven by effective learning, which selects the most pertinent pieces of data to classify and add to the instructional pool.

Global Federated Learning for Industrial IoT Market Segmentation: By Region

• North America
• Europe
• Asia-Pacific
• The Middle East & Africa
• South America

In 2022, the Europe region held the largest share in the global federated learning for the industrial IoT market. Applications, such as patient data and risk analysis, medical imaging and diagnostics, precision medicine, lifestyle management and monitoring, inpatient care and hospital management, virtual assistant, and research, comprise the federated learning market for healthcare. The process of discovering new drugs is time-consuming because it requires the researcher to look at a lot of bioscience data, like patents and genomic data, as well as a lot of publications that are added daily to all biomedical journals and databases. Because of this, the drug discovery procedure needs to change, and federated learning can change and improve this procedure. As a result, new products are being developed by market vendors to provide a better platform for everyone. The difficulties posed by aging populations and a lack of healthcare professionals are increasing the use of AI technologies in healthcare in Europe. 

Global Federated Learning for Industrial IoT Market Key Players:

1. Google LLC (United States)
2. Microsoft Corporation (United States)
3. IBM Corporation (United States)
4. Enveil (United States)
5. DataFleets (United States)
6. NVIDIA Corporation (United States)
7. FedML (United States)
8. Secure AI Labs (United States)
9. Aptima, Inc. (United States)
10. Databloom AI (United States)