Global Federated Learning Solutions Market By Application (Network Automation, Virtualization & Cloud, Data Center Transformation, Network Security, Other Applications), By Industry Vertical (BFSI, Healthcare & Life Sciences, Retail & E-Commerce, Manufacturing, Energy and Utilities), By Region and Key Companies - Industry Segment Outlook, Market Assessment, Competition Scenario, Trends and Forecast 2021–2031

Introduction –

Federated Learning (FL) is a machine learning technique that allows an algorithm to be trained over a large number of decentralized edge devices and servers that store local data without the need to swap data. For example, Google just released its federated learning tool, which is the first of its type and capable of providing a variety of applications such as context suggestion, item ranking on equipment, and  Next word prediction.

In the healthcare and pharmaceutical industries, companies may improve their business models and make effective use of AI to boost profitability. Furthermore, federated learning systems have the ability to bring new predictive capabilities to smart equipment, allowing consumers to have a consistent experience while protecting their personal information.

FL offers several benefits as well as challenges. Benefits such as it allow devices like smartphones to learn a shared prediction model cooperatively while keeping training data on a device rather than uploading and storing it on a central server.

Model training is moved to edge, which includes devices such as smartphones, tablets, IoT, and organizations akin to hospitals that are supposed to function under tight privacy regulations. A significant security advantage is to keep personal data local.

Apart from this, FL has certain challenges as well, such as in FL networks, communication is a key bottleneck as data generated on each device remains local. To train a model using data supplied by network devices, communication-efficient methods must be developed that limit the overall number of communication rounds, while iteratively sending small models up the network.

Detailed Segmentation –

Global Federated Learning Solutions Market is Segmented on the Basis of Application, Industry Vertical, and region. Represented below is a detailed segmental description:

Based on Application:

• Network Automation
• Virtualization & Cloud
• Data Center Transformation
• Network Security
• Other Applications

Based on Industry Vertical:

• BFSI
• Healthcare & Life Sciences
• Retail & E-Commerce
• Manufacturing
• Energy and Utilities

Based on Region

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

Market Dynamics –

Major corporations are further researching FL solutions, which are crucial in the support of privacy-sensitive applications, where training data is distributed at the edge. By sharing model changes, FL helps to protect consumers’ data. Data privacy and security are increasingly becoming important to businesses, where FL strategies have proven to be effective. Data silos and a focus on data privacy are now major AI concerns, but FL could be a potential solution.

It may provide a unified paradigm for various businesses while safeguarding local and sensitive data, allowing them to benefit from each other without having to worry about data privacy. In the manner that technology approaches learning, FL has garnered a lot of attention. When it comes to FL, there are two types of privacy: global and local.

The model changes created at each round must be kept private from all untrusted third parties save the central server in order to maintain global privacy. Local privacy, on the other hand, necessitates that the updates remain private to the server as well.  Factors such as these are slated to play a pivotal role in influencing the revenue trajectory of this global industry over the next decade.

The shortage of skilled individuals, including IT professionals, is a fundamental difficulty that most firms face when adopting machine learning into their respective business processes. Employees find it challenging to grasp and use FL models for training data because it is a novel idea.

This is due to a lack of employee training on how to utilize FL solutions. Certain industries need to build more specific skill sets and job titles, i.e., engineers who can handle and comprehend the new FL architecture necessary for deploying and maintaining machine learning models.

Competitive Landscape –

Key players –

• Cloudera Inc.
• Consilient
• DataFleets
• Decentralized Machine Learning
• Edge Delta
• Enveil
• Extreme Vision
• Google
• IBM
• Intellegens
• Lifebit
• Microsoft
• NVIDIA
• Owkin
• Secure AI Labs
• Sherpa.ai
• WeBank

Key developments –

2021:

• NVIDIA introduced the NVIDIA AI Enterprise in March 2021, a full software suite of enterprise-grade AI tools and frameworks that run on VMware vSphere, and are optimized, certified, and maintained by NVIDIA. Customers can reduce AI model development time from 80 weeks to only eight weeks with NVIDIA’s AI Enterprise, and they can deploy and manage advanced AI applications on VMware vSphere.
• ZeroReveal 3.0 was released by Enveil in February 2021. It provides homomorphic encryption-powered capabilities through a decentralized and efficient framework that reduces risk and addresses business concerns such as, data sharing, collaboration, monetization, and regulatory compliance. The upgrades in the 3.0 release improve the integration and performance of the solution.

2020:

• NVIDIA Clara Train 3.1 will be released in November 2020 with a configurable authorization structure that improves security and ensures sensitive data remains secure. It also contains a new administrative tool that boosts researcher productivity by enabling a 10x increase in algorithm experimentation. The new capabilities in Clara Train 3.1 assist healthcare developers in scaling FL safely and increasing research output.

For the Federated Learning Solutions Market research study, the following years have been considered to estimate the market size: