Google Research has developed a new software library, FAX, that helps with big-scale computations for machine learning across different devices, such as computers and phones. It’s built on top of JAX, a tool for high-performance ML, and designed to facilitate working with federated learning.

Read the full paper here.

Why FAX Matters

.At its core, FAX is built upon JAX, a high-performance ML library. Leveraging JAX’s capabilities, FAX introduces several key features tailored to FL scenarios. This enables FAX to target both data center and cross-device applications, where computations may be distributed across various devices.

It provides tools to write and optimize ML computations easily. It uses techniques like automatic differentiation (AD) and just-in-time (JIT) compilation to make ML programs more efficient.

Additionally, FAX specifically targets federated learning, where devices collaborate on ML tasks without sharing data directly. This type of learning is important for privacy reasons and for handling large amounts of data.

Key Features

• Scalability: FAX can handle large models and distribute computations across many devices, making it possible to handle massive models that may not fit within the memory constraints of a single device.
• Easy Programming: It’s designed to make writing and optimising ML programs simple.
• Automatic Differentiation: FAX includes tools for automatic differentiation, which is crucial for training ML models efficiently.
• Production Integration: FAX can translate ML models into production systems that run on mobile devices.

While there are other frameworks for federated learning, FAX stands out because it combines scalability, federated AD, and integration with production systems in a single package. Other frameworks may focus on one or two of these aspects, but FAX aims to address all three simultaneously.

To sum up, FAX is a powerful tool for building and deploying ML models, particularly in scenarios where data privacy and scalability are important considerations.