Federated learning is a machine learning method that has an algorithm across several decentralized edge devices or servers carrying local data samples. This method is in contrast with the traditional centralized machine learning techniques where all the local datasets are stored to a single server. Additionally, this technique makes sure that the local data samples are identically dispersed in the server. Federated learning can be used to construct models on consumer behavior from the information pool of smart phones without disclosing personal data such as for next-word prediction, face detection, voice recognition, and others. Federated learning allows multiple vendors to build a common, machine learning model without sharing data, thus allowing to address important issues such as data privacy & security, data access rights, and the ability to access diverse data. It is being used in various industries, including defense, telecommunications, and pharmaceuticals.
COVID-19 Scenario Analysis:
- Rising instances of COVID-19 around the world are causing economic slowdown and thousands of workers are working from home during this pandemic. Artificial intelligence and machine learning have been implemented to predict and examine the growth of possible data warnings in countries around the world. Artificial intelligence plays a key role in understanding the forecast of future of COVID-19 pandemic across nations by using real-time information.
- The implementation of federated learning solutions by the healthcare sector to predict the disease and its medicine has seen growth in the pandemic situation. The key market players are using these solutions to assist healthcare organizations understand drug effectiveness differences from patient to patient, identifying the best drug used for the right patient at the right time, enhancing the drug development process as well as improving treatment outcomes.
Top impacting factors: Market Scenario Analysis, Trends, Drivers, and Impact Analysis
The rise in need to improve learning between devices and organizations, the rise in need to ensure better data security and privacy, and the growing need to adapt data in real-time to optimize conversions automatically are the factors driving the growth of the federated learning solutions market. In addition, these solutions help corporations to leverage machine learning models by keeping data on devices, thereby propelling the federated learning solutions market growth.
However, lack of skilled technical expertise and interoperability issues in the system are the factors hampering the growth of the market. Furthermore, the ability to facilitate predictive features on smart devices without affecting the customer experience and disclosing private information are providing lucrative opportunities to the growth of the federated learning solutions market during the forecast period.
The market trends for federated learning solutions market are as follows:
Implementation of solutions by manufacturing industry vertical:
The manufacturing segment is expected to implement federated learning solutions, owing to increasing emphasis on industrial internet of things and rise in competition. Manufacturing companies are highlighting the analysis of data collected from multiple sources, including websites, mobile, retail outlets, and social media. These collected data are used to examine the performance of the machines and suggest ways to improve the efficiency to reduce cost of overall operations.
North America to dominate the market:
North America is expected to dominate the market during the forecast period. The dominance is achieved due to the presence of developed countries, including the U.S. and Canada. The adoption and implementation of federated learning solutions is achieved due to strict data regulations and focus on innovation by research, focus on data privacy, and rapid technology infrastructure advancements among end users. The increased adoption of developing technologies, such as artificial intelligence, machine learning, big data analytics, and internet of things, is expected to drive the market growth in the region.
Key benefits of the report:
- This study presents analytical depiction of the federated learning solutions market along with the current trends and future estimations to determine the imminent investment pockets.
- The report presents information related to key drivers, restraints, and opportunities along with detailed analysis of the market share.
- The current market is quantitatively analyzed to highlight the federated learning solutions market growth scenario.
- Porter’s five forces analysis illustrates the potency of buyers & suppliers in the market.
- The report provides a detailed market analysis based on the present and future competitive intensity of the market.
Questions answered in the federated learning solutions market research report:
- Who are the leading market players active in federated learning solutions market?
- What would be the detailed impact of COVID-19 on federated learning solutions market?
- What current trends would influence the market in the next few years??
- What are the driving factors, restraints, and opportunities in the federated learning solutions market?
- What are the projections for the future that would help in taking further strategic steps?
Federated Learning Solutions Market Report Highlights
By Enterprise Size
By Industry Vertical
Key Market Players
DataFleets Ltd, Nvidia Corporation, Intellegens Ltd., Cloudera Inc, Microsoft Corporation, Alphabet Inc, Owkin Inc., Enveil Inc., International Business Machines Corporation, Edge Delta Inc.