Truvace – tracking AI's real-world impact with sourced reporting

Truvace reports that artificial intelligence progress is hindered by isolated data islands and growing privacy concerns, proposing secure federated learning as a solution. The framework, building on Google's 2016 model, enables organizations to collaborate on AI without compromising user privacy.

Federated Machine Learning /item/federated-machine-learning Today’s artificial intelligence still faces two major challenges. One is that, in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to these challenges: secure federated learning. Beyond the federated-learning framework first proposed by Google in 2016, we introduce a comprehensive secure federated-learning framework, which includes horizontal federated learning, vertical federated learning, and federated tran… ACM Transactions on Intelligent Systems and Technology https://doi.org/10.1145/3298981 Secure federated learning allows organizations to build data networks and share knowledge without compromising user privacy. AI progress is blocked because industry data remains in isolated islands and privacy and security constraints are strengthening.