Flower is an open-source framework that enables organizations to train artificial intelligence (AI) models on distributed data using federated learning. Rather than bringing all data to a central server, Flower allows companies to improve their AI by training directly on sensitive or siloed data that resides across organizations or user devices.
The Technology Behind Flower
Federated learning is a machine learning technique designed to address privacy, security, and data sovereignty concerns by training models where the data is generated—such as on devices or within company silos—without moving the raw data itself. Flower provides the infrastructure to orchestrate this decentralized training process, making it accessible for both research and production environments. This approach is significant because most AI today relies on centralized public datasets, which represent only a small portion of the world's data. By enabling training on far more diverse and distributed data, Flower aims to unlock new advances in AI capabilities.
Who Uses Flower?
Flower is used by organizations that need to leverage sensitive or distributed data for AI development without compromising privacy or regulatory requirements. Notable users include:
- Banking Circle
- Nokia
- Porsche
- Brave
These companies use Flower to enhance their AI models on data that cannot be readily centralized, such as financial records, proprietary automotive data, or privacy-sensitive browser information.
Competitive Landscape: Federated Learning Platforms and Alternatives
Flower operates in the federated learning and distributed AI training space. Key competitors and related platforms include:
- Google Federated Learning: One of the pioneers of federated learning, providing frameworks and research for distributed AI model training.
- IBM Federated Learning: IBM offers federated learning as part of their AI and enterprise data privacy solutions.
- NVIDIA Federated Learning: NVIDIA provides federated learning tools, often integrated with their GPU-accelerated platforms.
- Bitfount: A newer entrant focused on privacy-preserving distributed model training.
Other notable resources and educational providers in this field include DeepLearning.AI, Splunk, and Netguru.
Flower distinguishes itself by being fully open-source and designed for flexible integration across research and production, making it accessible to a broad range of users in the B2B sector and beyond.
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