FAX: Scalable and Differentiable Federated Primitives in JAX (AI summary)

Key Points

1. The paper introduces FAX, a JAX-based library designed for large-scale distributed and federated computations in data center and cross-device applications.

2. FAX leverages JAX’s sharding mechanisms and provides building blocks for federated computations as primitives, enabling translation to XLA HLO and implementation of federated automatic differentiation.

3. The ability of FAX to scale abstractly written compute-intensive programs across large distributed compute environments is a key factor in the success of modern machine learning, especially for ML computations involving large language models.

4. FAX is designed to support federated learning (FL) computations, where clients collaborate on an ML task without sharing data, and provides features for performant and scalable data center computation, easy and extensible algorithm expression, and automated translation to production federated systems.

5. FAX can be used to express, shard, and run a wide range of ML computations in the data center, including parallel and distributed algorithms such as FedAvg, FedOpt, branch-train-merge, DiLoCo, and PAPA.

6. The paper discusses FAX’s implementation in JAX, including how federated values and computations are represented, how FAX computations are sharded across data center runtimes, and how federated automatic differentiation is implemented.

7. The authors present numerical evidence of the scalability and efficiency of FAX, demonstrating its near-constant runtime for a fixed model size and its weak scaling behavior.

8. FAX's ability to interpret computations to production platforms is discussed, highlighting how FAX preserves data placement information and integrates federated automatic differentiation for the translation to production systems.

9. The paper concludes by mentioning future work areas for FAX, including generalizations of federated AD to non-linear communication primitives and extensions of FAX to more general data placements, and interpreters for specific production systems.

Summary

The paper presents FAX, a JAX-based library designed for large-scale distributed and federated computations in data center and cross-device applications. FAX leverages JAX's sharding mechanisms to enable native targeting of TPUs and state-of-the-art runtimes, including Pathways. It embeds building blocks for federated computations as primitives in JAX, allowing translation to XLA HLO and implementing federated automatic differentiation. This facilitates interpretation into existing production cross-device federated compute systems. The authors highlight FAX as an easily programmable, performant, and scalable framework for federated computations in the data center.

The ability to scale abstractly written compute-intensive programs across large distributed environments is crucial for the success of modern machine learning, especially for computations involving large language models. FAX aims to bring the benefits of sharding, easy-to-use JIT compilation, and federated automatic differentiation to federated learning, a distributed learning paradigm where clients collaborate on machine learning tasks without sharing data.
FAX achieves this by integrating federated building blocks into JAX using JAX's Primitive mechanism, allowing powerful features like sharding and JIT-compilation to XLA. FAX enables efficient and scalable federated training of language models and can be interpreted into Python-independent computation graphs usable by production federated learning systems. The paper provides empirical evidence of FAX's scalability and efficiency, demonstrating near-constant runtime for fixed model sizes, even with increasing cohort sizes and TPU chips. The authors illustrate the need for federated AD, as it simplifies the development of sophisticated federated learning algorithms and aids in privacy-preserving mechanisms. The paper also discusses FAX's interpretable representations, which enable its computations to be automatically translated to production platforms.

FAX offers a comprehensive framework for large-scale distributed and federated computations, enabling powerful features such as efficient sharding, federated automatic differentiation, and seamless integration with production federated learning systems. The authors highlight the potential of federated AD in accelerating the development and research of distributed and parallel machine learning algorithms.

Reference: https://arxiv.org/abs/2403.07128