Federated Learning: AI without Data Sharing in Healthcare and Beyond
Training AI models in regulated industries is increasingly complex. Data privacy laws, institutional silos, and security risks make centralized data sharing both impractical and non-compliant. ACL Digital’s latest whitepaper explains how Federated Learning (FL) solves these challenges by enabling secure, multi-party model training without moving raw data. From healthcare to finance, FL supports collaboration while ensuring privacy, compliance, and organizational control.
What will you learn?
- Technical frameworks for coordinating decentralized model training across hospitals, financial institutions, and edge devices.
- Real-world outcomes from use cases like collaborative medical imaging (NVIDIA Clara), global pharmaceutical research (MELLODDY), and mobile personalization (Google Gboard).
- Security-enhancing techniques such as Differential Privacy, Secure Aggregation, and Homomorphic Encryption designed to satisfy HIPAA, GDPR, and other regulatory mandates
Why download this whitepaper?
If your business is exploring a privacy-first approach to AI that supports compliance, collaboration, and secure data practices, this whitepaper offers actionable guidance to help you evaluate, plan, and implement federated learning effectively. Download now.
