Flower lands $3.6M to grow its platform for federated learning

Daniel Beutel, a tech entrepreneur and researcher at the University of Cambridge, co-founded a startup called Flower to address the limitations of AI training on public data. Flower uses federated learning to train AI models on distributed data across thousands of devices and locations, without the data leaving its source. The platform recently launched FedGPT, a federated approach to training large language models on data spread around the world, respecting privacy and data leakage concerns.

Flower has seen significant uptake, with over 2,300 developers and several Fortune 500 companies and academic institutions using the platform. The startup, a member of one of Y Combinator’s 2023 cohorts, raised $3.6 million in its pre-seed round from investors like First Spark Ventures, Hugging Face CEO Clem Delangue, Factorial Capital, Betaworks, and Pioneer Fund. The funds will be used to expand Flower’s core team and accelerate the development of its open-source software.

Key takeaways:

Daniel Beutel, co-founder of startup Flower, believes the reliance on public data for AI training is limiting the field, as distributed data from devices and organizational silos is much larger and more comprehensive.
Flower, founded in 2020, aims to decentralize AI training by allowing developers to train models on data spread across thousands of devices and locations using a technique called federated learning.
Flower recently launched FedGPT, a federated approach to training large language models (LLMs) that allows organizations to use internal, sensitive data without sharing them with an LLM provider.
Flower has seen significant uptake, with a community of over 2,300 developers and users including Fortune 500 companies and academic institutions. The startup raised $3.6 million in its pre-seed round.

Flower lands $3.6M to grow its platform for federated learning | TechCrunch

Key takeaways:

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