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cs.LG2023
Leveraging Function Space Aggregation for Federated Learning at Scale
Nikita Dhawan, Nicole Mitchell, Zachary Charles +2
The federated learning paradigm has motivated the development of methods for aggregating multiple client updates into a global server model, without sharing client data. Many feder…
cs.LG2023
Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning
Zachary Charles, Nicole Mitchell, Krishna Pillutla +2
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.…