Federated Learning Solutions - Global Market Outlook (2021 - 2028)
発行: Stratistics Market Research Consulting
ページ情報: 英文 200+ Pages
According to Stratistics MRC, the Global Federated Learning Solutions Market is accounted for $94.28 million in 2021 and is expected to reach $227.36 million by 2028 growing at a CAGR of 13.4% during the forecast period. Federated Learning is a machine learning setting where the objective is to train a high-quality unified model with training data distributed over a large number of clients each with unreliable and relatively slow network connections. Federated learning (also known as collaborative learning) is a machine learning technique that trains an algorithm across multiple decentralized edge devices or servers holding local data samples, without switching them.
Ability to ensure better data privacy and security by training algorithms on decentralized devices
Federated learning is being researched by major companies and plays a critical role in supporting privacy-sensitive applications where the training data are distributed at the edge. Federated learning takes a step toward protecting users' data by sharing model updates. Companies can no longer ignore the growing importance of data privacy and data security. The approach of federated learning has provided a new paradigm for applications leveraging data. Currently, data silos and the focus on data privacy are important challenges for AI, but federated learning could be a solution. It could establish a united model for multiple organizations while the local and sensitive data is protected so that they could benefit together without having to worry about data privacy. Federated learning has received a lot of attention in the way the technology tackles the challenge of protecting users' privacy by decoupling of data provisioned at end-user equipment and Machine Learning (ML) model aggregation, such as network parameters of deep learning at a centralized server. With federated learning, privacy can be classified in two ways: global privacy and local privacy. Global privacy necessitates that the model updates generated at each round are private to all untrusted third parties other than the central server. At the same time, local privacy further requires that the updates are also private to the server.
Lack of skilled technical expertise
The major issue confronting most organizations while incorporating ML in their business processes is the lack of skilled employees, including IT experts. Since federated learning is a new concept, it becomes difficult for employees to understand and implement federated learning models for training data. This is due to the lack of training provided to employees for implementing federated learning models. Recruiting and retaining technical resources have become a significant focus for several enterprises due to the lack of skilled people to develop and execute federated learning projects that involve complex techniques, such as ML. For example, organizations need engineers who can handle and understand the new federated learning architecture involved with deploying and maintaining ML models.
Capability to enable predictive features on smart devices
Mobile phones, wearable devices, and autonomous vehicles are just a few of the modern distributed networks generating a wealth of data each day. Owing to the growing computational power of these devices-coupled with concerns related to transmitting private information-it is increasingly attractive to store data locally and push network computation to the edge devices. Federated learning is an emerging approach that helps companies easily collect and store data. Federated learning has the potential to enable predictive features on smartphones without diminishing the user experience or leaking private information. Edge devices, such as smartphones and IoT devices, can benefit from the on-device data without the data ever leaving the device, especially for computationally constrained devices where communication is a bottleneck with smaller devices. Today, industries, such as BFSI, healthcare and life sciences, and retail and eCommerce, collect gigantic amounts of data generated by consumer devices, including mobile phones, tablets, and personal laptops, on a daily basis. The federated learning approach provides a unique way to build such personalized models without intruding users' privacy.
Indirect information leakage
Privacy concerns serve to motivate the desire to keep raw data on each local device in a distributed Machine Learning (ML) setting. However, sharing other information such as model updates as part of the training process brings up another concern-the potential to leak sensitive user information. For instance, it is possible to extract sensitive text patterns, such as a credit card number, from a Recurrent Neural Network (RNN) trained on the user data. Unlike differential privacy protection, the data and the model itself are not transmitted, nor can they be guessed by the other party's data. Hence, there is a little possibility of leakage at the raw data level. Federated learning exposes intermediate results, such as parameter updates from an optimization algorithm, such as Stochastic Gradient Descent (SGD). However, no security guarantee is provided, and the leakage of these gradients may actually reveal important information when exposed together with data structure, such as in the case of image pixels.
The manufacturing segment is expected to have the highest CAGR during the forecast period
The manufacturing segment is growing at the highest CAGR in the market. Smart manufacturing technologies are extensively accepted by manufacturers to advance the proficiency and efficiency of the industrial process while guaranteeing a high level of safety. In today's competitive environment growing focus on IIoT with advances in artificial intelligence and machine learning manufacturers can access big data and use learning algorithms to analyze the data. But, the privacy of sensitive data for industries and manufacturing companies is a significant factor. Federated learning algorithms can be useful to these problems as they do not access or reveal any sensitive data.
The healthcare and life sciences segment is expected to be the largest during the forecast period
The healthcare and life sciences segment is expected to be the largest share in the market. 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.
Region with highest share:
Asia Pacific is projected to hold the largest share in the market due to the increasing adoption of advanced technologies in various industries. The demand for federal learning solutions has been increasing with advanced technologies such as AI, IoT, and big data analytics to analyze the collected data. Moreover, emerging industrialization and ongoing development for data regulations in countries like India, China, and Japan are expected to create many lucrative opportunities for the federal learning solutions market.
Region with highest CAGR:
Europe is projected to have the highest CAGR in the market due to the increased adoption of technologies and the presence of a large number of federal learning solution vendors in the region. Other factors like strict data regulations and increasing demand for data privacy is expected to boost the market in Europe.
Key players in the market:
Some of the key players profiled in the Federated Learning Solutions Market include Cloudera, Consilient, DataFleets, Decentralized Machine Learning, Edge Delta, Enveil, Extreme Vision, Google, IBM, Intellegens, Lifebit, Microsoft, NVIDIA, Owkin, and Secure AI Labs.
In March 2021: NVIDIA launched the NVIDIA AI Enterprise, a comprehensive software suite of enterprise-grade AI tools and frameworks optimized, certified, and supported by NVIDIA that run on VMware vSphere. NVIDIA AI Enterprise enables customers to reduce AI model development time from 80 weeks to just eight weeks and allows them to deploy and manage advanced AI applications on VMware vSphere.
In February 2021: Enveil introduced new version of ZeroReveal 3.0. It delivers the homomorphic encryption-powered capabilities through an efficient and decentralized framework designed to reduce risk and address business challenges, including data sharing, collaboration, monetization, and regulatory compliance.
In November 2020: NVIDIA Clara Train 3.1 introduces a flexible authorization framework that enhances security to ensure sensitive data is protected. It also includes a new administration tool that enables a 10x increase in algorithm experimentation to boost researcher productivity. Clara Train 3.1 new features help healthcare developers scale federated learning securely and boost research productivity.
In May 2020: Owkin launched the COVID-19 Open AI Consortium (COAI). The consortium will enable advanced collaborative research and accelerate the clinical development of effective treatments for patients who are infected with COVID-19. In this project, Owkin used federated learning, aiming to help healthcare companies understand why drug efficacy varies from patient-to-patient, enhance the drug development process, and identify the best drug for the right patient at the right time, to improve treatment outcomes.
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