Apheris, the leading provider of secure federated data networks for life sciences, is partnering with Flower Labs.

BERLIN, BERLIN, GERMANY, March 24, 2025 /EINPresswire.com/ -- Apheris, the leading provider of secure federated data networks for life sciences, is partnering with Flower Labs, the developer of the industry’s most popular federated learning (FL) framework. This collaboration brings together Apheris’ secure, privacy-preserving infrastructure with Flower’s seamless ML user experience and advanced federated technology, making it easier than ever for organizations to adopt federated AI while ensuring robust privacy and security controls.

The best AI models will be federated because public data alone is often insufficient for training models that are both highly accurate and generalizable. However, successful federated AI requires a highly adaptive privacy framework that meets the needs of data custodians while preserving data utility for sensitive data to be contributed to collaborative settings. The Apheris-Flower partnership addresses these challenges by combining Apheris’ AI privacy research and governance controls with Flower’s leading federated learning technology, scalability and developer ecosystem.

Production-Grade Federated AI with Flower Labs
Flower has long been celebrated for its unmatched ease-of-use, large-active developer community and industry-firsts like Flower Intelligence and FlowerLLM. The Flower framework simplifies the building of even the most advanced FL systems in the following ways:

- Scalable and Customizable to Enterprise Environments: The underlying Flower architecture is built for high-performance with large-scale production environments in mind; the framework also offers out-of-the-box flexibility to support custom enterprise requirements such as aggregation and learning algorithms, full workload isolation, user authentication, networking protocols and data formats.

- Best-in-Class PETs Support: Flower offers AI developers a rich set of privacy-enhanced technologies (PETs) including many varieties of differential privacy, secure aggregation, homomorphic encryption to name a few, that are optimized under Flower for a wide range of ML hardware platforms.

- Hardware-agnostic Deployments: To enable federations across multiple organizations that often have hardware from different vendors in place, Flower supports running Federated Learning across heterogeneous environments.

Diverse ML Framework and Tool Compatibility: Flower seamlessly works with PyTorch, TensorFlow, XGBoost, MLX, JAX, and many more ML tools which simplifies adoption by existing AI teams, and the integration effort even for organizations employing diverse AI/ML toolchains.

Federated Data Networks for Life Sciences with Apheris

Apheris powers some of the largest federated life sciences data networks, addressing the critical challenge of making proprietary data accessible while ensuring IP protection and privacy. Publicly available datasets are insufficient to train high-quality ML models that meet industry requirements. The Apheris product addresses this by enabling life sciences organizations to collaboratively train higher quality models on complementary data from multiple parties.

- Data Stays Home, Computations Visit: Apheris enables secure computation on remote datasets without moving raw data, ensuring IP and privacy protection.
Certifiably Secure Deployments: Deployed in production environments with leading pharmaceutical companies, Apheris undergoes regular penetration testing and meets the highest security and compliance standards, including ISO 27001, SOC 2 Type II, GDPR, and the EU AI Act.
- Multi-Layered Privacy Controls: Granular governance over data, users, and computations on the algorithmic level to optimize privacy and data utility.
- Full Traceability & Auditability: Detailed logging ensures accountability and compliance.

By integrating with Flower Labs, Apheris connects its users to the most popular ML engineering experience and federated learning engine.
Shaping the Future of Federated AI

“The best AI models will be built on federated data, but this approach demands an infrastructure that balances privacy, security, and usability,” said Daniel J. Beutel, CEO of Flower Labs. “Flower’s scalability, ease of use and active developer community perfectly complement Apheris’ robust security and governance controls, making federated AI accessible at scale.”

“For life sciences AI to succeed, we need to enable developers to build and scale models over boundaries,” said Robin Roehm, CEO of Apheris. “By partnering with Flower, we're making powerful privacy-preserving data networks accessible with the most developer-friendly FL framework available.”
The partnership between Apheris and Flower Labs marks a significant step toward making federated AI more accessible, scalable, and secure for organizations worldwide.

For more information, visit www.apheris.com or www.flower.ai.

Contact: press@apheris.com or hello@flower.ai

Marie Röhm
Apheris I GmbH
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