Federated Learning: A Privacy-Preserving Approach to Training AI Models

As artificial intelligence (AI) continues to evolve, federated learning (FL) is gaining traction due to its ability to protect privacy while training powerful machine learning models. Here’s everything you need to know about this innovative technology.

What is Federated Learning, Non-Technically Speaking?

In simple terms, federated learning allows different devices or organisations to collaborate on building a machine learning model without ever sharing their actual data. The model learns separately on each device, and only the learning outcomes (not the data) are combined to create a stronger, more accurate model. This helps maintain privacy while still improving AI performance.

What is Federated Learning, Technically Speaking?

Federated learning is a decentralised method of training machine learning models without centralising the raw data. Instead of moving all the data to a single location, the model is trained locally on edge devices or within individual data silos. Updates to the model’s parameters (gradients or weights) are shared with a central server, which aggregates them to form a global model. This approach reduces the risk of exposing sensitive data while still benefiting from distributed data across multiple devices or institutions.

What is Federated Learning? (Explain It Like I'm 5)

Imagine a group of people each making their own sandwich. Instead of sharing their ingredients, they each make their sandwich at home. Afterward, they tell the group what changes made their sandwich taste better—like adding more cheese or toasting the bread.?

Everyone uses this shared advice to improve their own sandwich, but no one has to reveal their original ingredients. Federated learning works the same way—devices use their own data to improve a model and only share improvements, not the data itself.

When Would You Use Federated Learning?

Federated learning is useful when you need to train machine learning models on decentralised data that can’t be easily shared, such as sensitive medical records or data across different organisations. It’s perfect for privacy-preserving machine learning, especially in cases where transferring large datasets is impractical or forbidden due to legal regulations.

Theoretical Use Cases

1. Healthcare: Hospitals can train machine learning models on patient data across different locations without sharing private health information. Each hospital’s data stays local, but they collaboratively improve the accuracy of the model.
2. Mobile Devices: Federated learning allows companies like Google to improve their AI models by learning from data on individual smartphones without needing to upload personal user information to a central server.
3. Financial Institutions: Banks can train fraud detection models on data from multiple institutions without revealing customer information, improving the accuracy of fraud detection algorithms while maintaining privacy.

Examples in the Wild

• Google’s Gboard: One of the most well-known examples of federated learning is its use in Google’s Gboard, the smartphone keyboard. The model learns how users type and suggests better autocorrections without Google ever seeing individual users’ typing data.
• Autonomous Vehicles: Federated learning is being used in the development of self-driving cars. Different cars collect data about driving conditions locally and train models on this data without sharing it with a central server. Only the improvements to the driving model are shared back, helping improve the overall AI system without risking privacy or transferring massive amounts of data. This approach ensures that self-driving cars can learn from diverse environments while keeping sensitive location data and driving patterns private.

Who Implements It?

With the availability of libraries like TensorFlow Federated and Flower, software engineers and developers can also implement federated learning without deep expertise in machine learning. These tools simplify the process of setting up and coordinating federated models, helping to address the communication issues between the nodes. While data scientists still design and optimise the models, and machine learning engineers manage the technical aspects of aggregation and system heterogeneity, software engineers and developers can now focus on integrating these frameworks into applications, ensuring scalability, and handling system requirements. Privacy engineers may also play a role to ensure compliance, and safety of communications by the use of Secure Multi Party Computations or Homeomorphic Encryption along the critical path of the model.

What Available Libraries Are There to Take the Work Out of It?

Several libraries and frameworks make federated learning implementation easier:

• Flower: A flexible framework that supports various machine learning libraries and simplifies building federated learning systems across devices and servers.
• TensorFlow Federated: An open-source framework designed for integrating federated learning into TensorFlow models.
• PySyft: A Python library that enables secure, privacy-preserving federated learning.
• FATE: A platform built for industrial federated AI model training, providing tools for large-scale collaboration.

These tools streamline federated learning setups, allowing teams to focus on model development and integration.

What Does This Unlock?

Federated learning unlocks the potential to train AI models on highly sensitive or decentralised datasets without compromising privacy or confidentiality. It enables collaboration across multiple devices or institutions without needing to centralise or expose raw data. This approach helps to mitigate privacy risks while still producing highly accurate machine learning models.

What Are the Downsides?

• Communication Overhead: Federated learning requires constant communication between edge devices and the central server, which can slow down the process and introduce delays.
• Security Concerns: If privacy-preserving techniques (such as secure aggregation) aren’t implemented correctly, model updates could potentially leak sensitive information to the central server. The same concerns about data flow in distributed systems, such as poisoning and backdoor attacks, must be applied to the training pipeline.
• Data Heterogeneity: Variations in data quality across different devices or organisations can lead to biased or inaccurate models.
• System heterogeneity: the difference of equipment may affect the efficiency of the whole training process. The training pipeline must deal with subjects like asynchronous communication, device sampling, model heterogeneity and build a fault-tolerant mechanism.

How Difficult Is It to Do?

Federated learning, while conceptually straightforward, involves several technical challenges. Implementing it from scratch requires strong knowledge of machine learning, privacy techniques, and distributed systems. However, the availability of frameworks like TensorFlow Federated, Flower, and PySyft makes it significantly easier to adopt, though privacy engineers and machine learning experts are still needed to ensure proper implementation.

TL;DR

Federated learning is becoming an increasingly valuable tool in AI, offering a way to train models on decentralised data without compromising privacy. Its applications are growing, particularly in industries like healthcare and finance, where data privacy is crucial. Although there are challenges, such as communication overhead and the need for robust privacy mechanisms, federated learning is a promising approach for privacy-preserving machine learning.