How Federated Learning works? Clearly Explained|
Summary
TLDRThe video script discusses the limitations of traditional centralized machine learning models, highlighting privacy concerns and the challenges of personalization. It introduces Federated Learning as a decentralized solution, allowing models to learn from data without compromising user privacy. The script explains how this approach works, emphasizing its benefits for industries like healthcare and its potential to revolutionize AI training, while acknowledging its limitations and the technical challenges overcome to make it viable.
Takeaways
- 🔒 Traditional machine learning requires centralized data, raising privacy concerns due to regulations like HIPAA and GDPR.
- 📈 Machine learning models benefit from more data, leading to better accuracy and personalization, but this can be challenging with privacy restrictions.
- 🌐 Federated learning offers a decentralized approach to machine learning, allowing models to learn from data without centralizing it.
- 💡 The concept of federated learning is similar to a client-server model, where computations are distributed across devices.
- 📲 The advancement in mobile processors with AI capabilities since 2018 has enabled local machine learning on edge devices.
- 🛠 Federated learning works by training models on local data, then sending only the model updates to a central server, preserving data privacy.
- 🔑 The updates sent to the central server are summaries of changes, not the raw data, ensuring that user data remains confidential.
- 🏥 Federated learning is particularly beneficial in healthcare, allowing sensitive data to stay at the source while still benefiting from AI advancements.
- 🛑 Federated learning can tackle challenges in various industries by providing better data diversity without compromising privacy.
- 🚀 Large-scale projects are underway to apply federated learning to drug discovery and improve AI at the point of care.
- 🤖 Google uses federated learning to enhance on-device machine learning models for features like voice commands in Google Assistant.
- 🔄 Federated learning requires overcoming technical challenges, such as the need for efficient algorithms to handle updates from diverse devices.
Q & A
What is the central premise of traditional machine learning models?
-The central premise of traditional machine learning models is that data must be centralized, meaning data from various sources like mobile phones and laptops is aggregated and stored on a single centralized server for training the model.
Why is data privacy a concern in the context of centralized machine learning?
-Data privacy is a concern because regulations like the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR) restrict access to user data, making it challenging to extract, compile, and store user data on centralized servers for machine learning model training.
How does the lack of personalization in machine learning applications affect user adaptability?
-If machine learning applications are not built by training on large user data, they often result in poor and non-personalized results, leading to less adaptability by the user community.
What is Federated Learning and how does it differ from traditional machine learning?
-Federated Learning is a decentralized form of machine learning that overcomes the challenges of centralized data training by distributing computations between a central server and multiple devices. Unlike traditional machine learning, it allows training models on data without accessing the data directly by bringing the model to the data instead of bringing the data to the model.
How has the computational capability of edge devices evolved to support Federated Learning?
-The computational capabilities of edge devices have significantly increased with the introduction of AI-powered chips in 2018, enabling these devices to run machine learning models locally, which was previously limited due to modest computational capabilities.
How does Federated Learning ensure privacy while training models?
-Federated Learning ensures privacy by keeping the raw data on the user's device. Only the learnings or updates from the model, not the actual data, are shared with the central server in an encrypted manner, preserving data privacy.
What is the process of model training in Federated Learning?
-In Federated Learning, a device downloads the current model, improves it by learning from its local data, summarizes the changes, and sends this update back to the central server. The server then averages these updates with others to improve the shared model, without storing individual updates in the cloud.
How can Federated Learning benefit the healthcare and health insurance industry?
-Federated Learning can benefit the healthcare and health insurance industry by allowing the protection of sensitive data at its original source and providing better data diversity by gathering data from various locations, such as hospitals and electronic health record databases, for diagnosing rare diseases or improving drug discovery.
What is an example of a large-scale Federated Learning project in the healthcare sector?
-An example is the Melody drug discovery consortium in the UK, which aims to demonstrate that Federated Learning techniques could provide pharmaceutical partners with the ability to leverage the world's largest collaborative drug compound data set for AI training without sacrificing data privacy.
How does Federated Learning apply to improving on-device machine learning models for user behavior?
-Federated Learning can be used to build models on user behavior from a data pool of smartphones without leaking personal data, such as for next word prediction, face detection, and voice recognition. Google uses Federated Learning to improve on-device machine learning models like 'Hey Google' in Google Assistant.
What are some technical challenges that had to be overcome to make Federated Learning possible?
-To make Federated Learning possible, challenges such as algorithmic efficiency, bandwidth and latency limitations, and the need for high-quality updates on edge devices had to be addressed. The Federated Averaging algorithm was developed to train deep networks using less communication compared to traditional methods.
What are some limitations of Federated Learning?
-Federated Learning has limitations such as the model size, which should not be too large to run on edge devices, and the relevance of data present on user devices to the application. It cannot be applied to solve all machine learning problems.
Outlines
🤖 Federated Learning: Overcoming Privacy Concerns in Machine Learning
The first paragraph discusses the traditional centralized approach to machine learning, where data from various sources is aggregated on a central server for training models. This method, however, raises significant privacy concerns, especially with regulations like HIPAA and GDPR limiting access to user data. The paragraph introduces Federated Learning as a solution to these challenges, a decentralized method that allows models to learn from data without needing to centralize it. It highlights the historical limitations due to computational power and the turning point in 2018 with AI-powered mobile processors. The beauty of Federated Learning is its ability to train models on non-accessible data by bringing the model to the data instead, ensuring that raw data never leaves the user's device, preserving privacy while enhancing model accuracy through collective updates.
🛡️ Federated Learning: Enhancing Data Privacy and Real-time Predictions
The second paragraph delves into the practical applications of Federated Learning, particularly in industries like healthcare and insurance, where data privacy is paramount. It discusses how Federated Learning can be used to improve models for rare disease diagnosis and drug discovery without compromising data privacy. The paragraph also touches on the use of Federated Learning in improving on-device functionalities like voice recognition in Google Assistant. It explains the technical aspects of Federated Learning, such as the use of the Federated Averaging algorithm to overcome bandwidth and latency issues. The limitations of Federated Learning are briefly mentioned, noting that it requires relevant data on user devices and may not be suitable for very large models. The paragraph concludes by emphasizing the potential of Federated Learning to revolutionize machine learning and address current AI challenges.
Mindmap
Keywords
💡Centralized Data
💡Privacy Concerns
💡Federated Learning
💡Edge Devices
💡Data Privacy
💡Healthcare and AI
💡Federated Averaging
💡Real-time Prediction
💡Technical Challenges
💡Data Relevance
Highlights
Traditional machine learning requires centralized data, which leads to privacy concerns.
Centralized training of ML models aggregates user data on a single server, raising data privacy issues.
Data privacy regulations like HIPAA and GDPR restrict access to user data by organizations.
Federated learning is a decentralized approach to machine learning that addresses privacy concerns.
Federated learning allows training models on data without access, by bringing the model to the data.
Computational capabilities of mobile devices have improved, enabling local machine learning model training.
Federated learning preserves privacy by only sharing learnings, not raw data.
The process involves devices learning independently and contributing updates to a central model.
Federated learning updates are sent to the cloud using encrypted communication.
Healthcare and insurance industries can benefit from federated learning by protecting sensitive data.
Federated learning can improve data diversity for diagnosing rare diseases and drug discovery.
Google uses federated learning to enhance on-device machine learning models for features like voice commands.
Federated learning enables real-time predictions on edge devices, keeping user data confidential.
Technical challenges in federated learning include algorithmic complexity and data relevance.
Federated averaging algorithm addresses bandwidth and latency issues in federated learning.
Federated learning cannot be applied to all problems; model size and data relevance are considerations.
Federated learning offers a revolutionary approach to machine learning, addressing data privacy and AI challenges.
Transcripts
traditional machine learning models operate on a central premise data must be centralized means we
usually train our data that is aggregated from several Edge devices like mobile phones laptops
Etc and is brought together to a centralized server machine learning algorithms then grab
this data and trains itself and finally predicts results for new data generated this means that
data from various users is extracted aggregated and then stored on a single centralized class CL
server for training the model it's a bit like Gathering all the ingredients for a recipe in
one place before you start cooking however this approach has its drawbacks privacy concerns are
at the Forefront of these issues in an era where data privacy is increasingly critical
some of these are health insurance portability and accountability act in the healthcare sector and
general data protection regulation these restrict access to user data by any organization now the
question arises is it acceptable to extract user data compile it from numerous users and stack
them up on a centralized Cloud Server for machine learning model training so what do organizations
that thrive off personal data do it is getting more and more difficult for startups and companies
to build applications that could provide better personalized results to users all ml applications
work on simple Logic the more data you feed it the more accurate it gets the better and more
personalized results it returns if not built by training on large user data these often result
in poor and non-personalized results this leads to less adaptability of the new applications by
the user Community these challenges for both the user and for the organizations can be addressed
with the help of Federated learning so let dive into Federated learning Federated learning is a
decentralized form of machine learning a novel approach to overcome the challenges of machine
learning it's akin to the client server framework of old Distributing computations between a central
server and multiple devices think of it as a team of detectives each working on their piece
of the puzzle contributing to the final solution historically the use of Federated learning was
limited due to the modest computational capabilities of mobile wearable and Edge
devices they were simply not strong enough to run machine learning models locally however the
tide turned in 2018 with the introduction of the first mobile processor powered by AI chips these
chips packed a punch significantly increasing the computational horsepower of these devices
the beauty of Federated learning lies in its ability to train models on data to which we
don't have access instead of bringing the data to the model we bring the model to the data it's
a revolutionary shift in how we approach machine learning so how does Federated learning actually
work well imagine your device as a personal tutor it learns from the data on your device and uses
this knowledge to refine its teaching method or in technical terms the machine learning model this
model is then given a makeover a summary of the changes it is has undergone and this update is all
that's sent back to the central server it's a bit like a group study session each device studies on
its own but contributes its knowledge back to the group creating a collective understanding
that's greater than the sum of its parts and what about privacy that's the best part your
data never leaves your device the only thing shared is the learning not the raw data this
makes Federated learning a privacy preserving model training approach it works like this your
device downloads the current model improves it by learning from data on your phone and
then summarizes the changes as a small focused update only this update to the model is sent to
the cloud using encrypted communication where it is immediately averaged with other user updates
to improve the shared model all the training data remains on your device and no individual updates
are stored in the cloud Federated learning has the potential to tackle some of the challenges
Federated learning can revolutionize how AI models are trained and this could benefit fits the
different business Healthcare and health insurance industry can take advantage of Federated learning
because it allows protecting sensitive data in the original Source Federated learning models
can provide better data diversity by gathering data from various locations for example hospitals
electronic health record databases to diagnose rare diseases some large-scale Federated learning
projects are in progress hoping to improve drug Discovery and bring AI benefits to the point of
care Melody a drug discovery consorti based in the UK aims to demonstrate that Federated
learning techniques could give pharmaceutical Partners The Best of Both Worlds the ability
to leverage the world's largest collaborative drug compound data set for AI training without
sacrificing data privacy Kings College London is hoping that its work with Federated learning as
part of its London Medical Imaging and artificial intelligence Center for value-based healthcare
project could lead to breakthroughs in classifying stroke and neurological impairments determining
the underlying causes of cancers and recommending the best treatment for patients Federated learning
can be used to build models on user behavior from a data pool of smartphones without leaking
personal data such as for next word prediction face detection voice recognition etc for example
Google uses Federated learning to improve on device machine learning models like Hey Google
in Google Assistant which allows users to issue voice commands Federated learning brings machine
learning to the edge making real-time prediction a reality imagine a network of smartphones each
learning from its user and collectively they become a Powerhouse of knowledge all while
keeping your data confidential Federated learning then is not just a solution to a problem but a
revolutionary approach to machine learning that is changing the game to make Federated learning
possible we had to overcome many algorithmic and Technical challenges in a typical machine
Learning System an optimization algorithm like stochastic gradient descent SGD runs on a large
data set partitioned homogeneously across servers in the cloud such highly iterative
algorithms require low latency High throughput connections to the training data these bandwidth
and latency limitations can be taken care by using the Federated averaging algorithm which can train
deep networks using less communication compared to a naively Federated version of SGD the key
idea is to use the powerful processors in modern mobile devices to compute higher quality updates
than simple gradient steps Federated learning have some limitations and cannot be applied to solve
all machine learning problems model should not be too large to run on the edge devices and data
present on the user devices should be relevant to the application in recent times isolation of
data and emphasis on data privacy are some of the challenges of artificial intelligence Federated
learning brings New Hope and if implemented correctly can help to cater the need of the lot
of business problems this is Tech Genie signing off and please subscribe to the channel if you
want to learn more about Federated learning join our channel the tech Brewery for further
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