Federated learning can be described as a machine learning approach that distributes an algorithm among a number of decentralized end devices or servers that each have local data samples. This strategy differs from standard centralized machine learning methods, which store all local datasets on a single server.
New York, July 01, 2022 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Federated Learning Market Size, Share & Industry Trends Analysis Report By Application, By Vertical, By Regional Outlook and Forecast, 2022 – 2028" - https://www.reportlinker.com/p06289244/?utm_source=GNW
Additionally, this technique ensures that the local data samples are disseminated to the server in the same way. Federated learning can be utilized to build consumer behavior models from the data pool of smartphones without revealing personal information, like for next-word prediction, voice recognition, facial identification, and other applications. Federated learning enables various vendors to develop a shared machine learning algorithm without sharing data, allowing crucial issues like data access rights, data privacy and security, and the capacity to access heterogeneous data to be addressed. Defense, telecommunications, and medicines are among the businesses that can leverage federated learning to optimize their operations.
The growing need for improved data protection and privacy, as well as the increasing requirement to adapt data in real-time to optimize conversions automatically are driving the advancement of the federated learning solutions market. Moreover, by retaining data on devices, these solutions assist organizations in leveraging machine learning models, boosting the federated learning market forward. Furthermore, the ability to provide predictive features on the latest smart devices without compromising the consumer experience or divulging private information is providing lucrative opportunities for the federated learning market to develop throughout the coming years. Additionally, this technique ensures that the local data samples are disseminated to the server in the same way. Federated learning can be utilized to build consumer behavior models from the data pool of smartphones without revealing personal information, like for next-word prediction, voice recognition, facial identification, and other applications. Federated learning enables various vendors to develop a shared machine learning algorithm without sharing data, allowing crucial issues like data access rights, data privacy and security, and the capacity to access heterogeneous data to be addressed. Defense, telecommunications, and medicines are among the businesses that can leverage federated learning to optimize their operations.
The growing need for improved data protection and privacy, as well as the increasing requirement to adapt data in real-time to optimize conversions automatically are driving the advancement of the federated learning solutions market. Moreover, by retaining data on devices, these solutions assist organizations in leveraging machine learning models, boosting the federated learning market forward. Furthermore, the ability to provide predictive features on the latest smart devices without compromising the consumer experience or divulging private information is providing lucrative opportunities for the federated learning market to develop throughout the coming years.
COVID-19 is an unprecedented global public health crisis that has impacted practically every business, and its long-term repercussions significantly impacted various markets in numerous countries all over the world. In addition, governments across the world imposed lockdown in their countries in order to regulate the diffusion of the hazardous COVID-19 infection. These lockdowns caused major disruptions in the worldwide supply chain of all the products and services due to travel restrictions. The infection was rapidly spreading all over the world, creating economic stagnation and compelling thousands of employees to work from home. However, artificial intelligence, as well as machine learning, were majorly used to forecast and investigate the spread of potential data alarms in several countries all over the world.
Market Growth Factors
Enhanced data privacy in numerous applications
Due to federated learning, the manner in which ML approaches are offered is evolving. Companies are increasing their efforts on performing a thorough investigation of federated learning. Using federated learning, companies may reinforce their existing algorithms and improve their AI applications. The demand for improved learning is increasing among both gadgets and companies. In the healthcare field, federated learning could help healthcare personnel deliver high-quality outcomes while also accelerating drug development. For example, FADNet, a new peer-to-peer technique, is a remedy for centralized learning inadequacies.
Enables collaborative learning among various users
Federated learning, rather than keeping data on a single computer or data mart, stores data on original sources, like smartphones, manufacturing detection equipment, other end devices, and machine learning machines are trained on the go. This aids in decision-making before being sent back to a centralized computer. For example, federated learning is widely used in the finance sector for debt risk assessments. Typically, banks use whitelisting processes to keep customers out of the Federal Reserve System based on their credit card information. Risk assessment variables, like taxation and reputation, may be employed by working with other financial institutions and eCommerce businesses.
Market Restraining Factors
Scarcity of skilled technical professionals
Many businesses encounter a significant impediment when integrating machine learning into existing workflows due to a scarcity of trained people, particularly IT specialists. Because federated learning systems are a new concept, it is difficult for personnel to grasp and execute them. Recruiting and maintaining technical skills became a major concern for several firms due to a scarcity of skilled candidates to incorporate federated learning projects that include difficult methodologies, such as machine learning. As an organization, they must develop a growing range of talents and job titles. Organizations, for example, require experts that can administer and comprehend the current federated learning architecture connected with the installation and maintenance of machine learning algorithms.
Based on Application, the market is segmented into Drug Discovery, Risk Management, Online Visual Object Detection, Data Privacy & Security Management, Industrial Internet of Things, Augmented Reality/Virtual Reality, Shopping Experience Personalization, and Others. In 2021, the industrial internet of thigs segment procured a substantial revenue share of the federated learning market. Sensors are used in modern IoT networks, like wearable gadgets, autonomous vehicles, and smart homes, to gather and respond to the data in real-time. To operate properly, a fleet of autonomous vehicles, for example, may require an updated model of construction, traffic, or pedestrian behavior. Due to privacy concerns and the restricted connectivity of each device, constructing aggregate models in these cases may be challenging. Federated learning approaches make it possible to train models that can respond to changes in these systems quickly while respecting users’ privacy. This factor is segmenting the growth of this segment.
Based on Vertical, the market is segmented into Healthcare & Life Sciences, BFSI, IT & Telecommunication, Energy & Utilities, Manufacturing, Automotive & Transportation, Retail & Ecommerce, and Others. In 2021, the healthcare & life sciences segment witnessed the largest revenue share of the federated learning market. The increasing growth of this segment is attributed to the fact that healthcare and life sciences industry is constantly under pressure to improve the quality of services it provides to people. The amount of unstructured data in the healthcare industry is increasing significantly. Access to unstructured data, like medical device output, imaging reports, and lab findings, is ineffective in improving patient health. Pharmaceutical firms are included in the healthcare and life sciences category. With many research initiatives, consortiums, and implementations, the utilization of federated learning technologies is expediting in the healthcare and life sciences sectors.
Based on Regions, the market is segmented into North America, Europe, Asia Pacific, and Latin America, Middle East & Africa. In 2021, Europe accounted for the largest revenue share of the federated learning market. Patient data and risk analysis, precision medicine, lifestyle management, and monitoring, medical imaging and diagnostics, drug development, virtual assistant, inpatient care and hospital management, wearable, and research are some of the applications in the federated learning industry for healthcare. The drug development process is time-consuming, as it necessitates the analysis of massive amounts of bioscience data, such as patents, genetic data, and a significant number of papers uploaded daily throughout all biomedical journals as well as databases.
The major strategies followed by the market participants are Product Launches. Based on the Analysis presented in the Cardinal matrix; Microsoft Corporation and Google, Inc. are the forerunners in the Federated Learning Market. Companies such as Nvidia Corporation, IBM are some of the key innovators in the Market.
The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include IBM Corporation, Microsoft Corporation, Intel Corporation, Google LLC, Cloudera, Inc., NVIDIA Corporation, Edge Delta, Inc., DataFleets Ltd. (LiveRamp Holdings, Inc.), Enveil, and Secure AI Labs, Inc.
Recent Strategies Deployed in Federated Learning Market
Partnership, Collaborations, and Agreements
Apr-2021: Cloudera joined hands with NVIDIA and Apache Spark 3.0. This collaboration aimed to enable scalable and accelerated big data workflows and pre-processing without code changes with the incorporation of the RAPIDS Accelerator for Apache Spark 3.0 along with NVIDIA’s computing expertise into its Cloudera Data Platform.
May-2020: Intel collaborated with the University of Pennsylvania, a private Ivy League research university in Philadelphia. This collaboration aimed to offer a federation of 30 institutes to leverage federated learning in order to train AI models to detect the boundaries of brain tumors.
Product Launches and Product Expansions
Dec-2021: Nvidia rolled out FLARE, an open-source software platform. FLARE, or Federated Learning Application Runtime Environment, aimed to provide a mutual computing foundation for federated learning. Moreover, the new solution would also underpin Clara Train’s federated learning software.
Nov-2021: Google introduced federated learning in its Smart Text Selection. With this launch, the company aimed to facilitate the process of training the neural network model across user interactions with increased reliability and user privacy. In addition, new improvements would enable the models to be trained on-device on real interactions by leveraging federated learning.
Oct-2021: Google unveiled FedJAX, an open-source library based on JAX. This launch aimed to expedite and streamline the process of developing and evaluating federated algorithms. Moreover, the new solution would also work as simple building blocks for the deployment of federated algorithms, models, prepackaged datasets, and faster simulation speed.
Jul-2021: Edge Delta launched an open demo environment. The new solution aimed to enable users to freely explore a fully functional environment, real-time insights being generated, and the value of the live continuous streaming data-based platform without the requirement for payment details and login credentials.
2021-May-2021: NVIDIA unveiled Clara Train 4.0, an application framework. This launch aimed to offer a foundation for medical imaging, which comprises AI-Assisted Annotation, Federated Learning, AI-Assisted Annotation, and AutoML. In addition, Clara Train would also strengthen the company’s underlying infrastructure from TensorFlow to MONAI.
Apr-2021: IBM introduced new capabilities into its IBM Watson. Through this product expansion, the company aimed to expand Watson tools, which are developed in order to aid enterprises in explaining and governing AI-led decisions. Moreover, the new capabilities would also allow businesses to increase insight precision and minimize risks in order to fulfill their compliance and privacy requirements.
Jul-2020: IBM introduced IBM Federated Learning on Github. With this launch, the company aimed to offer a framework to its customers in order to enable them to boost their model training through the data aggregated from several sources while maintaining data privacy.
Apr-2020:Enveil rolled out ZeroReveal, an encrypted machine learning product. The new product would allow businesses to process data against authenticated machine learning model. In addition, the new Enveil ZeroReveal ML is built on its ZeroReveal Search solution and would change the secure data usage model by enabling businesses to experience advanced decision making via collaborative and federated machine learning with more privacy and security.
Scope of the Study
Market Segments covered in the Report:
• Drug Discovery
• Risk Management
• Online Visual Object Detection
• Data Privacy & Security Management
• Industrial Internet of Things
• Augmented Reality/Virtual Reality
• Shopping Experience Personalization
• Healthcare & Life Sciences
• IT & Telecommunication
• Energy & Utilities
• Automotive & Transportation
• Retail & Ecommerce
• North America
o Rest of North America
o Rest of Europe
• Asia Pacific
o South Korea
o Rest of Asia Pacific
o Saudi Arabia
o South Africa
o Rest of LAMEA
• IBM Corporation
• Microsoft Corporation
• Intel Corporation
• Google LLC
• Cloudera, Inc.
• NVIDIA Corporation
• Edge Delta, Inc.
• DataFleets Ltd. (LiveRamp Holdings, Inc.)
• Secure AI Labs, Inc.
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