Decentralized Collaborative AI With Federated Learning in Trustworthy Environments

Federated learning is a decentralized and privacy-preserving approach for AI, where data from multiple locations are used to train a shared model without exchanging the training data. It's particularly beneficial in cases where data is sparse, confidential, or lacks diversity. In addition to the security features NVFLARE already use, introducing H100 Confidential Computing in the infrastructure further strengthens the privacy-preserving approach by explicitly verifying the trustworthiness of the environment participants use.