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Excited to share the release of my first paper Google AI with Johannes Ballé, Zachary Charles and Jakub Konečný! arxiv.org/abs/2201.02664 We leverage rate-distortion theory to achieve competitive accuracy at a fraction of the communication cost in federated learning.

Couldn’t agree more

A great intro in Federated Learning and how it allows ML training with privacy by default

Do you want an introduction to federated learning with great visualizations and an interactive sandbox? Look no further. Thanks to Nicole Mitchell and Adam Pearce for their hard work, this is really exciting!

Some really great visualizations and demonstrations by Adam Pearce and Nicole Mitchell around the key ideas behind Federated Learning, and how it enables learning communally-useful ML models without ever bringing all the raw data together.

"How Federated Learning Protects Privacy" Nicole Mitchell (Nicole Mitchell) and Adam Pearce (Adam Pearce) share how Federated Learning enables collaboration between institutions, helps us understand big systems, and better protects our privacy 4/18

While most #ML models are trained by collecting data on a central server, federated learning makes it possible to train models without any user's raw data leaving their device. Check out the latest AI Explorable on how federated learning protects privacy→ pair.withgoogle.com/explorables/fe…

1/ Our work is out! 🚨 Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning We push federated learning research closer to LLM scales. Paper: arxiv.org/abs/2307.09619 Joint with Nicole Mitchell & Krishna Pillutla Thread below.

Now accepted to NeurIPS 2023! Excited to chat about it there with you all.

Leveraging Function Space Aggregation for Federated Learning at Scale Nikita Dhawan, Nicole Elyse Mitchell, Zachary Charles, Zachary Garrett, Gintare Karolina Dziugaite. Action editor: Anastasios Kyrillidis. openreview.net/forum?id=Ytp9K… #fedfish #federated #benc