How Gboard Suggests Emojis: A Technical Overview

When an emoji pops up in Gboard as a suggestion, have you ever wondered where that suggestion comes from or how Gboard arrives at it? Often, the emoji seems appropriate, though sometimes it doesn't. 😎

	Figure: Gboard's predictive emoji suggestions powered by federated machine learning, demonstrating real-time emoji predictions based on text input.

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Gboard suggests emojis to users as part of its intelligent text prediction system, powered by machine learning.

Here's how it works:

• Gboard analyzes your typed text in real-time to suggest emojis that match your message. For example, if you type "happy birthday," it might suggest a 🎂 or 🎉

• Over time, Gboard learns your typing habits and emoji preferences to make more relevant suggestions

• You can also search for specific emojis by typing related words - like typing "smile" to find 😊 or 😄

• The system uses federated learning, which ensures your personalization happens locally on your device without compromising privacy

Initial Training Data

Before federated learning begins, Google trains the initial, global model on massive, anonymized datasets. Here's a breakdown of these sources:

Publicly Available Text Corpora

These include:

• Books from projects like Project Gutenberg

• News articles from various sources

• Vast amounts of filtered, publicly available web content

• Wikipedia articles across various languages

• Common Crawl dataset of web crawl data

• Anonymized search queries, processed to remove personally identifiable information

• Other anonymized text data from various Google services

Key Considerations for Initial Training Data

The initial training data is carefully selected based on several factors:

Anonymization: All data undergoes rigorous anonymization to remove personally identifiable information through techniques like removing names, addresses, and sensitive details.

Diversity: Datasets represent various writing styles, topics, and languages to help the model generalize well.

Quality: Careful curation and filtering ensure data quality and minimize biases.

Size: The datasets contain billions or trillions of words for rich language understanding.

How Book and Wikipedia Training Helps Emoji Suggestions

Training on books, Wikipedia, and similar datasets provides a foundational understanding of language that helps Gboard suggest emojis effectively:

Learning Language Patterns

The model develops understanding of:

• Syntax and grammar rules

• Word relationships, including synonyms and antonyms

• Words frequently used together

Building Contextual Understanding

The model learns:

• Semantic context of words in various situations

• Associations between words and sentiments

• Cultural references and idiomatic expressions

Linking Concepts to Emojis

This foundation enables the model to:

• Map sentiments to appropriate emojis

• Understand cultural references and their emoji representations

• Connect real-world scenarios with relevant emoji suggestions

Federated Learning: Personalizing Suggestions Privately

While foundational training provides general language understanding, real-world emoji suggestions require personalization through federated learning.

How Federated Learning Works

The process involves:

• Local model updates based on your emoji usage patterns

• Secure aggregation of encrypted updates from many devices

• Global model improvement without accessing individual user data

Benefits of Federated Learning

This approach offers several advantages:

• Preserves privacy by keeping sensitive data on your device

• Enables personalization based on individual preferences

• Adapts to regional and cultural differences in emoji usage

• Provides continuous improvement through collective learning

For example, if you frequently type "coffee" and select ☕, federated learning recognizes this pattern locally. When combined with millions of other users' patterns, the global model improves at suggesting ☕ for everyone who types "coffee."

Privacy Protection Mechanisms

Gboard implements several advanced privacy protections:

Secure Aggregation: Updates are combined with other users' data before server transmission.

Invertible Bloom Lookup Tables (IBLTs): An IBLT is similar to a Bloom filter—a fast checklist that checks whether something might be in a dataset—but more advanced. Unlike a Bloom filter, IBLTs can list their contents and enable efficient reconciliation of data across devices while protecting privacy.

Differential Privacy: Random noise is added to updates to protect unique patterns.

These advanced techniques allow Gboard to learn and improve globally while ensuring individual data remains secure and private.

Emoji Prediction in Practice

After the foundational training, the model can make sophisticated predictions:

• Connect "birthday" with 🎂 or 🎉 based on common patterns

• Link emotional words like "sad" with 😢 or 😔

• Understand cultural or thematic connections, such as "sunset" suggesting 🌅

How Machine Learning Works in Practice

The system combines its initial training with real-world learning:

Local Learning Process:

• Gboard observes how you interact with emojis on your device

• It tracks which emojis you select for specific words or phrases

• The system notes the frequency of emoji usage in different contexts

• All this learning happens directly on your device

Global Improvement Process:

• Encrypted updates about general patterns (not specific usage) are sent to central servers

• These updates are combined with data from millions of other users

• The improved model is distributed back to all devices through app updates

Example of Learning New Emoji Patterns

Let's look at how Gboard learns to suggest the 🔥 emoji when people type "lit" (slang for "great"):

1. Your device notices you often type "lit" and select 🔥

2. This pattern is encoded using special privacy-protecting structures (IBLTs)

3. The encoded pattern joins updates from other users through secure aggregation

4. The central server improves the global model without knowing individual usage

5. All users receive better emoji suggestions through model updates

This balance of local learning and global improvement ensures that Gboard gets smarter at suggesting emojis while maintaining strong privacy protections. The system continues to learn from collective usage patterns while keeping individual data secure and private.🔒

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Learn More Here: 💖 To learn more about federated learning and privacy in machine learning, check out Google's Privacy in Machine Learning Training: Federated Learning course on Google Cloud Skills Boost.

These hands-on resources are part of the Advanced: Generative AI for Developers Learning Path on Google Cloud Skills Boost, which includes a Responsible AI for Developers section covering both federated learning and differential privacy techniques.

Want a video walkthrough to solve the lab? ✨ Follow along with the Software Girls channel's hands-on lab walkthrough: Differential Privacy in Machine Learning with TensorFlow Privacy.