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TRV-2026-0212Certified recordPeer-reviewed

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…

Science · The Trace — both readings · certified 2026-07-13 · v1 · article view · machine-readable

Current reading — gain

Secure federated learning allows organizations to build data networks and share knowledge without compromising user privacy.

Current reading — problem

AI progress is blocked because industry data remains in isolated islands and privacy and security constraints are strengthening.

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Truvace Impact Record TRV-2026-0212, v1: “Federated Machine Learning.” Truvace, 2026-07-13. /record/TRV-2026-0212 (accessed at citation time). sha256 bff115a97f5e16f8…

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