In recent research, a team of Google Research researchers introduced FAX, an advanced software library built on top of JavaScript to improve computations used in federated learning (FL). It has been specifically developed to facilitate large-scale federated and distributed computations in various applications, including data centers and device-to-device situations.
Using JAX's chunking capabilities, FAX enables seamless integration with TPUs (Tensor Processing Units) and sophisticated JAX runtimes like Pathways. It provides numerous important benefits by directly incorporating the building blocks required for federated calculations as primitives within JAX.
The library provides scalability, simple JIT compilation, and AD functions. In FL, clients work together on machine learning (ML) tasks without revealing their personal information, and federated computations frequently include numerous client training models simultaneously while maintaining periodic synchronization. On-device clients can be used in FL applications, but high-performance data center software is still essential.
FAX overcomes these issues by providing a framework for specifying scalable federated and distributed computations in data centers. Through its Primitive mechanism, it incorporates a federated programming model in JAX, allowing FAX to use JIT compilation and fragmentation to XLA.
FAX has the ability to fragment calculations between models and clients, as well as data within the client between meshes of logical and physical devices. It makes use of innovations in the formation of distributed data centers such as Pathways and GSPMD. The team has shared that FAX can also provide Federated Automatic Differentiation (Federated AD) by facilitating direct and reverse mode differentiation through the JAX primitive mechanism. This allows data location information to be preserved during the differentiation process.
The team has summarized its main contributions as follows.
- Translation to XLA HLO (XLA High-Level Optimizer) format of FAX calculations is efficient. A domain-specific compiler called XLA HLO prepares computational graphics for use with a variety of hardware accelerators. By utilizing this feature, FAX can fully utilize hardware accelerators such as TPU, improving efficiency and performance.
- A comprehensive implementation of federated automated differentiation has been included in FAX. This feature automates the gradient calculation process through the intricate federated learning setup, significantly simplifying the expression of federated calculations. FAX accelerates the automatic differentiation process, which is a crucial part of training ML models, especially for federated learning tasks.
- FAX calculations are designed to work easily with the cross-device federated computing systems in use today. This means that calculations created with FAX, whether involving data center servers or on-device clients, can be quickly and easily implemented and carried out in real-world federated learning contexts.
In conclusion, FAX is flexible and can be used for various ML calculations in data centers. Beyond FL, it can handle a wide range of distributed and parallel algorithms, such as FedAvg, FedOpt, Branch-train-merge, DiLoCo, and PAPA.
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Tanya Malhotra is a final year student of University of Petroleum and Energy Studies, Dehradun, pursuing BTech in Computer Engineering with specialization in artificial intelligence and Machine Learning.
She is a Data Science enthusiast with good analytical and critical thinking, along with a burning interest in acquiring new skills, leading groups and managing work in an organized manner.
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