Databloom provides with Blossom Sky a secure and data regulation compliant FL framework. The founders of Databloom are the top experts in federated learning and distributed data processing and are also leading contributors to Apache Wayang, the Federated Data Processing Platform.
Blossom Sky allows you to connect to any data source without having to copy the data into data warehouses or data lakes. Extend your data pipelines and enrich your AI and ML models; even create Generative AI to improve data efficiency; create digital twins of your current environment and model any future scenario; and gain better and more accurate insight to be a data leader in your industry.
Blossom Sky allows you to connect to any data source without having to copy the data into data warehouses or data lakes. Extend your data pipelines and enrich your AI and ML models; even create Generative AI to improve data efficiency; create digital twins of your current environment and model any future scenario; and gain better and more accurate insight to be a data leader in your industry.
Blossom Sky integrates with all major data processing and streaming frameworks, as well as AI systems like Tensorflow, Pandas, PyTorch. We support JDBC as well as Java, Scala and Python interfaces and working on a native high-performance SQL layer, called SQL everywhere.
Explore the Potential of Federated Learning
Federated Learning (FL) is a technique for training machine learning models on decentralized data, where the data is distributed across multiple devices such as smartphones or edge devices. In FL, the data never leaves the device and the model is trained locally on each device. The updates to the model are then sent to a central server where they are aggregated to update the global model. This approach allows for training models on large amounts of data without the need to send the data to a central server, which can improve privacy and reduce the amount of data that needs to be transmitted.
FL can be used in a variety of applications, some examples are:
FL can be used in a variety of applications, some examples are:
- Personalization: Create personalized models for each user using data from their device, such as sensor readings, usage patterns, and browsing history.
- Privacy-sensitive applications: Train models on sensitive data, such as medical records, without requiring the data to be shared or stored in a centralized location.
- Mobile and IoT applications: Models can be trained on device data, such as sensor readings, to perform tasks such as image recognition, natural language processing, and anomaly detection.
- Healthcare: Train models using patient data to produce individualized treatment regimens or detect illnesses in their early stages.
- Autonomous vehicles: FL may be used to train models using sensor data from several vehicles in order to enhance self-driving car performance.
- Industry 4.0: Train models on sensor data from many machines to enhance the operation and maintenance of industrial systems.
- Edge computing: Models are trained on edge devices such as gateways and routers to accomplish tasks like as image recognition and anomaly detection.
