Federated Learning - FourWeekMBA

Federated learning is decentralized machine learning where models are trained on data from multiple edge devices, addressing privacy concerns. It allows devices like smartphones to learn without sending data to the cloud. Three types include horizontal, vertical, and federated transfer learning. Benefits include offline functionality, bandwidth savings, and personalization. Google’s Gboard on Android uses federated learning to improve user suggestions.

Table of Contents

Understanding federated learning

Traditional machine learning techniques require the training data from edge devices to be aggregated and centralized in a data center or machine. Machine learning algorithms then train themselves on the data and run the model on a cloud server where it can be accessed via various applications.

However, the traditional technique is subject to privacy concerns. Since tech giants such as Amazon, Microsoft, and Google offer cloud-based AI solutions, sensitive user data is sent to their servers where the models are trained.

Federated learning is one way to remedy this issue. Born at the intersection of blockchain, on-device AI, and edge computing, the approach involves training a centralized machine learning model on decentralized data.

How does federated learning work?

To understand how the process works, consider a smartphone. Federated learning enables smartphones to learn a shared prediction without the training data leaving the device. In other words, machine learning can take place without the need to store the data in the cloud.

Note that federated learning moves beyond local models that already make predictions on smartphones like the Mobile Vision API. This is because they enable model training to occur on the device as well.

When a smartphone downloads the current model, it improves it with data from the phone, and that improvement is summarized as a small update. Importantly, only the update is sent to the cloud and in any case, is encrypted and averaged with other user updates.

The three types of federated learning

There are three main types of federated learning:

Horizontal – where the central model is trained on similar datasets.
Vertical – where datasets are complementary. For example, book and movie reviews can be combined to predict someone’s music interests, and
Federated transfer learning – where a pre-trained model that performs one task is trained on a different dataset to perform another task. For example, banks could train an AI model to detect fraud and then repurpose it elsewhere.

Benefits of federated learning

Since models are trained on the device, applications can continue to function even when the device has no internet access. Users who are on metered connections will also appreciate the ability of federated learning to save them bandwidth.

What’s more, in many cases, on-device inference is far more energy-efficient than constantly sending data to the cloud. Since training data remains on the device, it can also be used to train models to deliver a personalized experience.

More detail is provided on this in the next section.

Federated learning and Gboard

Google is currently testing federated learning in Gboard on Android – otherwise known as the Google Keyboard. When Gboard shows a user a suggested query, the smartphone stores information on the current context and whether the query was clicked on.

Federated learning then processes a user’s search history and behavior on-device to deliver improvements the next time Gboard displays suggestions.

Key takeaways:

Federated learning is the decentralized form of machine learning where the model is trained on decentralized data across multiple edge devices.
When a device downloads the current model, it improves it with data with that improvement summarized as an update. Only the update is sent to the cloud and in any case, it is encrypted and averaged with other user updates to improve the model.
Federated learning has several benefits. Since models are trained on the device, applications can continue to function even when the device has no internet access. There are also improvements in bandwidth usage and the ability to deliver personalized experiences on devices.

Case Studies

Healthcare: Federated learning is used for privacy-preserving medical research and diagnostics. Hospitals and clinics can collaboratively train disease detection models on patient data without sharing sensitive information. For instance, a federated learning approach can be applied to develop AI models for early cancer detection based on medical imaging.
Smartphones: Companies like Apple have adopted federated learning to enhance features like predictive text and emoji suggestions. User data is kept on the device, and improved models are sent back to the device, ensuring privacy.
IoT Devices: In smart homes and cities, IoT devices collect vast amounts of data. Federated learning enables these devices to collaboratively optimize energy usage, traffic management, and security without transmitting personal data to central servers.
Financial Services: Banks and financial institutions use federated learning to build robust fraud detection models. Transactions and customer behaviors are analyzed locally, allowing for timely fraud prevention without sharing transaction details.
Retail and E-commerce: Retailers can employ federated learning to personalize product recommendations while respecting customer privacy. Each user’s device contributes to model training, leading to more accurate suggestions.
Autonomous Vehicles: Self-driving cars collect data on the road, which can be used to improve safety and navigation. Federated learning enables these vehicles to collaboratively train models without transmitting raw sensor data to a central server.
Manufacturing: In factories and industrial settings, federated learning optimizes production processes and equipment maintenance. Sensors on machinery collect data locally, helping identify performance issues and reducing downtime.
Energy Grids: Federated learning can be applied to manage and optimize energy grids in smart cities. Data from various sensors and meters are used to predict energy demands and allocate resources efficiently.
Language Translation: Language translation apps can use federated learning to improve translation accuracy. User interactions with translated text contribute to refining translation models without exposing individual sentences.
Agriculture: Precision agriculture leverages federated learning to enhance crop yield predictions. Data from sensors, drones, and weather stations are used collectively to optimize farming practices.
Cybersecurity: Federated learning aids in threat detection and cybersecurity. Anomalous network behavior patterns are analyzed across different organizations without sharing specific network data.
Entertainment Streaming: Streaming platforms can use federated learning to personalize content recommendations. Viewer interactions and preferences are considered while preserving user privacy.

Key Highlights

Definition: Federated learning is an advanced machine learning technique that facilitates model training on decentralized data sources, such as edge devices, without centralizing or sharing raw data.
Privacy Preservation: A key motivation behind federated learning is privacy preservation. It allows data to remain on individual devices, reducing the risk of data breaches or unauthorized access.
Data Distribution: Federated learning is ideal for scenarios where data is distributed across multiple devices, such as smartphones, IoT devices, or edge servers. Instead of centralizing data, models are trained locally on each device.
On-Device Model Updates: When a device participates in federated learning, it downloads the current machine learning model. It then refines the model using local data and generates model updates based on its own experiences and interactions.
Secure Data Transmission: Only the model updates, which are small and encrypted, are transmitted to a central server or aggregator. This ensures that sensitive or personal data remains secure.
Model Aggregation: At the central server, model updates from all participating devices are aggregated. This aggregated model becomes the new global model, which is then sent back to the devices.
Iterative Process: Federated learning often follows an iterative process. Devices repeatedly download the global model, improve it locally, and send updates. This process continues until the model reaches a desired level of performance.
Bandwidth Efficiency: Since only model updates are transmitted, federated learning reduces the amount of data sent over the network, making it bandwidth-efficient. This is especially valuable for users with limited data plans.
Energy Efficiency: On-device machine learning and local model updates are typically more energy-efficient than transmitting large datasets to a centralized server for processing.
Applications: Federated learning is applied in various domains, including:
- Healthcare: Analyzing medical data on patient devices while ensuring privacy.
- Smart Cities: Collecting data from IoT devices for urban planning without exposing personal information.
- Recommendation Systems: Personalizing content recommendations without revealing individual user preferences.
- Financial Services: Fraud detection models can be trained without sharing sensitive transaction data.
Customizable Models: Federated learning allows organizations to customize machine learning models for specific use cases while respecting data privacy.
Edge Computing: The decentralized nature of federated learning aligns well with edge computing, where data processing occurs closer to the data source, reducing latency and improving response times.
Challenges: Federated learning is not without challenges, including synchronization issues, communication overhead, and ensuring model fairness and accuracy across devices.
Google’s Federated Learning Example: Google’s Gboard uses federated learning to enhance search suggestions. User interactions with the keyboard help improve prediction models without sharing the content of messages.
Research and Development: Federated learning is an active area of research and development, with ongoing efforts to improve its scalability, security, and efficiency.