# Flower **Source:** https://geo.sig.ai/brands/flower **Vertical:** Infrastructure **Subcategory:** Cloud Services **Tier:** Challenger **Website:** flower.ai **Last Updated:** 2026-04-14 ## Summary SF YC W23 most popular open-source federated learning framework for privacy-preserving AI training; $20M Felicis Series A Feb 2024 serving Mozilla/Samsung/Bosch/Banking Circle competing with TensorFlow Federated for distributed training without centralizing sensitive data. ## Company Overview Flower is a San Francisco-based open-source federated learning framework company — backed by Y Combinator (W23) with $20 million in Series A funding in February 2024 led by Felicis Ventures with participation from First Spark Ventures, Mozilla Ventures, and angel investors including Clement Delangue (Hugging Face CEO), Scott Chacon (GitHub co-founder), and founders of Factorial and Betaworks — providing organizations, researchers, and developers with the world's most popular federated learning platform for training AI models on distributed data sources while maintaining data privacy and regulatory compliance, serving enterprise customers including Mozilla, Samsung, Bosch, Banking Circle, and Temenos. Founded in 2022, Flower enables organizations to train high-quality AI models across distributed datasets (patient records at multiple hospitals, financial transaction data across banks, user behavior data on user devices) without centralizing sensitive data into a single training environment. Flower's federated learning architecture inverts the traditional AI training data flow: in centralized AI training, raw data is collected from multiple sources into a central data lake where a model is trained — creating privacy violations, regulatory liability (GDPR, HIPAA, FINRA), and organizational data sovereignty concerns. Flower's federated learning sends the model to the data rather than bringing the data to the model: each participant (hospital, bank branch, device) trains the model locally on their own data, then shares only the model parameter updates (gradients) with the central coordinator, which aggregates the updates into an improved global model and distributes it back for the next round. The aggregation process ensures no individual data points can be reconstructed from the shared gradients — providing strong privacy guarantees for sensitive personal, medical, or financial data. Flower's framework (Python SDK, framework-agnostic — works with PyTorch, TensorFlow, JAX) handles the distributed communication, aggregation strategies, and client management that make federated learning practical at production scale. In 2025, Flower competes in the federated learning, privacy-preserving AI, and distributed machine learning market with Google's TensorFlow Federated (open-source, limited enterprise support), PySyft (OpenMined's federated learning library), and Apheris (federated AI for regulated industries, $15M raised) for enterprise and research federated learning platform adoption. The regulatory landscape has dramatically accelerated federated learning adoption: GDPR Article 17 (right to erasure) conflicts with centralized training data retention, HIPAA's de-identification requirements create barriers to healthcare AI model development on real patient data, and the EU AI Act's risk-based requirements for healthcare and financial AI create compliance complexity for centralized data collection. Mozilla, Samsung, and Bosch's enterprise deployments represent the three primary federated learning use cases: privacy-preserving browser telemetry analysis, on-device learning for smartphone AI features, and industrial machine sensor analytics without transmitting proprietary manufacturing data. The 2025 strategy focuses on growing the enterprise federated learning production deployments, building the Flower Intelligence managed platform (hosted FL infrastructure reducing deployment complexity), and expanding the healthcare consortium use cases for multi-institutional clinical AI development. ## Frequently Asked Questions ### What is Flower? Flower is a San Francisco-based AI platform company that builds the world's most popular open-source framework for federated learning. Founded in 2022, Flower enables organizations to train AI models on distributed data and compute resources while maintaining data privacy and security. ### What products and services does Flower offer? Flower offers a federated learning platform that provides an open-source ecosystem for training AI on distributed data. The platform enables secure AI model training on sensitive data distributed across organizational silos and user devices. ### Who are Flower's target customers? Flower targets enterprise organizations that need to train AI models on sensitive data. Current clients include trusted industry leaders such as Mozilla, Banking Circle, Samsung, Temenos, and Bosch. ### When was Flower founded? Flower was founded in 2022 and participated in Y Combinator's Winter 2023 (W23) batch. ### Where is Flower located? Flower is based in San Francisco, California, USA. ### What is Flower's recent funding history? Flower raised a $20M Series A round in February 2024, led by Felicis with participation from First Spark Ventures and Mozilla Ventures. Notable angel investors include Hugging Face CEO Clem Delangue, GitHub co-founder Scott Chacon, Factorial Capital, Betaworks, and Pioneer Fund. ### What are Flower's key achievements? Flower has built the world's most popular open-source framework for federated learning. The company serves major enterprise clients including Mozilla, Banking Circle, Samsung, Temenos, and Bosch. ### What technology approach does Flower use? Flower uses federated learning, a decentralized approach that enables AI training on distributed data and compute resources. This approach addresses data privacy and security concerns by allowing models to be trained on sensitive data without centralizing it, including data across organizational silos and user devices. ### How can organizations get started with Flower? Organizations can access Flower's open-source federated learning framework to train AI models on their distributed data. The platform is designed for enterprise organizations looking to improve AI models on sensitive data securely. ### What are Flower's most recent developments? In February 2024, Flower raised a $20M Series A funding round led by Felicis, with participation from First Spark Ventures, Mozilla Ventures, and notable angels from Hugging Face and GitHub. The company continues to grow its client base of industry leaders including Mozilla, Samsung, and Bosch. ## Tags ai-powered, b2b, developer-tools, infrastructure, platform, cloud-native, saas --- *Data from geo.sig.ai Brand Intelligence Database. Updated 2026-04-14.*