The convergence of blockchain, federated learning, and cloud AI addresses critical data privacy and security challenges. Neetu Gangwani, a renowned expert in technological innovations, highlights this approach as timely, meeting the needs of organizations aiming to uphold data confidentiality while advancing collaborative AI development.
Federated Learning: Decentralized Data for Centralized Intelligence
Federated learning (FL) allows collaborative AI training without sharing raw data, ensuring privacy and compliance with data laws. A central server coordinates updates, reducing data transfer and making FL suitable for regulated industries like healthcare and finance.
Blockchain: Building Transparent and Immutable Systems
Blockchain’s distributed ledger ensures transparency, security, and immutability by recording transactions in tamper-resistant blocks. Integrating blockchain with federated learning (FL) enables secure logging of model updates, making contributions visible and validated by network participants. This dual verification layer strengthens trust and mitigates risks of malicious interference, advancing collaborative AI.
The Power of Decentralized Model Updates
Blockchain-enabled federated learning treats model updates as transactions. …