Introduction:
Federated learning (FL) is a machine learning technique used to train a shared model on
multiple devices without exchanging their data. Instead, each device trains the model on
its local data and shares only any updates to model parameters with the central server;
then this central server aggregates updates from all devices and develops a global model
before sending back updates back out for another round of training on all of these
devices.
FL offers several advantages over traditional centralized machine learning approaches.
First, it protects user privacy by keeping data on devices instead of being sent centrally
for processing. Second, FL can be used to train models on large and distributed datasets
that would otherwise be difficult or impossible to centralize; finally it allows training
models on heterogeneous datasets gathered from multiple devices with different types
of information.
The Federated Learning Market was valued at USD 120.8 million in 2022. It’s predicted
to increase and become worth USD 311.4 million by 2032. The growth rate from 2022
to 2032 is estimated at 10.2% CAGR.
Below are top 10 Federated learning market:
1. Acuratio, Inc :
Acuratio, Inc. is an innovative technology company focused on healthcare and artificial
intelligence (AI). Their primary mission is creating AI-powered solutions that improve
medical diagnosis and treatment processes; using machine learning algorithms to
analyze vast amounts of medical data like images, patient records, lab results etc for
accurate disease detection. Their innovations could revolutionize healthcare delivery by
providing faster diagnosis with greater precision; saving lives while simultaneously
cutting costs - the transformative potential of AI is clear here! Acuratio's commitment to
healthcare AI underscores this potential.
2. Apheresis AI GmbH :
Apheresis AI GmbH stands at the forefront of AI in medical technology. Their speciality
lies in blood component separation through apheresis and they employ advanced
machine learning algorithms to optimize this medical procedure. By harnessing AI,
Apheresis AI strives to increase efficiency of blood component extraction while
improving patient outcomes - potentially revolutionizing this field with their innovations
that make apheresis safer and more effective across various medical applications.
3. Cloudera, Inc.:
Cloudera is an innovative American software company that specializes in big data
analytics and management. Their comprehensive platform for data management and
analysis offers organizations a means of efficiently handling vast quantities of
information. Their solutions leverage open-source technologies like Hadoop to gain
insight from data, enhance decision-making processes and drive innovation within
organizations utilizing its solutions - thus becoming key players within the big data
ecosystem.
4. Google LLC :
Google LLC, a subsidiary of Alphabet Inc., is an international technology giant known for
its internet-related products and services. Their impressive range includes the world's
most popular search engine, cloud computing services, the Android operating system,
software applications, hardware products and artificial intelligence research - integrating
these technologies into its products for enhanced user experiences such as more
personalized searches or computer vision detection - playing an instrumental role in
furthering AI research worldwide.
5. Enveil:
Enveil: Enveil is a cybersecurity firm specializing in homomorphic encryption technology.
Their innovative solutions enable data to be processed and analyzed while remaining
encrypted - offering significant gains to data security and privacy. Enveil's solutions have
proved particularly valuable to organizations handling sensitive or confidential
information such as financial institutions, healthcare providers and government agencies
that must comply with stringent data protection regulations.
6. Edge Delta Inc.:
Edge Delta Inc. specializes in optimizing and speeding up machine learning operations at
the edge of networks. Their solutions allow real-time data processing and model
inference on edge devices for reduced latency and bandwidth use - ideal for applications
like edge computing, IoT (Internet of Things), autonomous systems and similar scenarios
where quick decision making is essential. Their innovations enable organizations to use
AI without depending on cloud infrastructure - particularly useful when network
connectivity or latency requirements are limited or stringent.
7. FedML:
Federated Machine Learning, or FedML for short, is an area of AI research and
development specialized in collaborative machine learning models. FedML entails
training these models across various decentralized data sources while protecting privacy.
FedML has become widely adopted due to its potential to address privacy concerns and
data sharing restrictions imposed upon organizations by regulations; by enabling
organizations to collaborate without sharing sensitive data for training AI models
without sharing sensitive data directly between organizations collaborating, FedML
facilitates advancements across several domains including healthcare, finance,
recommendation systems etc; it plays an essential part of creating privacy-sensitive AI
technologies.
8. IBM Corporation:
As one of the world's premier technology companies, IBM Corporation boasts an
illustrious legacy in numerous areas - AI included. Their AI division specializes in creating
powerful solutions that assist businesses and governments make data-driven decisions
using powerful solutions like their Watson platform for natural language processing,
machine learning and data analytics. Their innovations can be found everywhere from
healthcare, finance and customer service industries; with commitments towards both
research advancement and practical applications demonstrating its significant influence
within this field.
9. Nvidia Corporation:
Nvidia is a renowned global technology company known for their graphics processing
units (GPUs) and their applications in artificial intelligence and deep learning. Nvidia
GPUs play an essential role in speeding AI workloads, including training complex neural
networks and powering high-performance computing tasks. Their GPUs have strong
presences within gaming, data centers, supercomputers and AI research. Their
dedication to advancement of GPU technology has had a substantial effect on AI and
machine learning fields alike; creating more powerful and efficient AI models than ever
before.
10. Intel Corporation:
Intel is a multinational technology company known for its semiconductor manufacturing
and innovation. Intel has made significant strides in artificial intelligence (AI), making
major contributions in developing high-performance CPUs and AI hardware accelerators
that are essential components in AI infrastructure that supports training and inference
tasks. Intel's commitment to improving AI hardware helps ensure solutions become
more accessible while handling complex workloads more easily - their technologies are
integrated into various devices from personal computers to data center servers
supporting various industries across different sectors.
Suggested reading:
Global Federated Learning Market By Deployment (Cloud and On-Premises), By
Applications (Industrial Internet of Things, Data Privacy Management, Drug Discovery,
Augmented and Virtual Reality, Risk Management, and Other Applications), By
Industry Vertical, By Region and Companies - Industry Segment Outlook, Market
Assessment, Competition Scenario, Trends, and Forecast 2023-2032
Recent development :
● Google AI Debuts Federated Learning Platform for Mobile Developers: Google AI
unveiled their federated learning platform ML Kit for Firebase to allow mobile
developers to train and deploy federated learning models without writing custom
code for Android and iOS apps.
● Microsoft Announces Federated Learning Toolkit for Azure: Microsoft recently
unveiled their federated learning toolkit for Azure called Azure Federated
Learning, providing developers with all of the tools required for training and
deploying federated learning models on Azure.
● IBM Unveils Federated Learning Solution for Healthcare: IBM has unveiled their
Watson Health Federated Learning solution, enabling healthcare organizations to
train federated learning models on their own data without sharing it with IBM.
● Amazon Web Services Announces Federated Learning Service: Amazon Web
Services (AWS) announced a federated learning service called Amazon SageMaker
Federated Learning to allow developers to train and deploy federated learning
models on AWS without needing to write custom code.
● Nvidia Unveils Federated Learning Library for GPUs: Nvidia recently unveiled its
Federated Learning Library for GPUs, providing developers with all of the tools
required for training and deploying federated learning models on Nvidia GPUs.