How Federated Learning works? Clearly Explained|

Summary

The 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.