Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning

Zachary Charles, Nicole Elyse Mitchell, Krishna Pillutla, Michael Reneer, Zachary Garrett

Keywords: federated learning, datasets, foundation models, large language models, meta-learning

TL;DR: We introduce a library for creating large-scale datasets with group-level structure, and use this to simulate federated learning at foundation model scales.

Abstract: We introduce Dataset Grouper, a library to create large-scale group-structured (e.g., federated) datasets, enabling federated learning simulation at the scale of foundation models. This library facilitates the creation of group-structured versions of existing datasets based on user-specified partitions, and directly leads to a variety of useful heterogeneous datasets that can be plugged into existing software frameworks. Dataset Grouper offers three key advantages. First, it scales to settings where even a single group's dataset is too large to fit in memory. Second, it provides flexibility, both in choosing the base (non-partitioned) dataset and in defining partitions. Finally, it is framework-agnostic. We empirically demonstrate that Dataset Grouper enables large-scale federated language modeling simulations on datasets that are orders of magnitude larger than in previous work, allowing for federated training of language models with hundreds of millions, and even billions, of parameters. Our experimental results show that algorithms like FedAvg operate more as meta-learning methods than as empirical risk minimization methods at this scale, suggesting their utility in downstream personalization and task-specific adaptation. Dataset Grouper is available at https://github.com/google-research/dataset_grouper.

Submission Number: 954