aceFederated 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…
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.
Evidence
- Peer-reviewedACM Transactions on Intelligent Systems and Technology2019-01-28
- Peer-reviewedSecurity and Communication Networks2022-09-28
- Peer-reviewedApplied Sciences2024-08-09
- Peer-reviewedArtificial Intelligence Review2026-02-28
- Peer-reviewedEnergies2025-09-24
How should this claim be treated?
Truvace Impact Record TRV-2026-0212, v5: “Federated Machine Learning.” Truvace, 2026-07-19. /record/TRV-2026-0212 (accessed at citation time). sha256 a43427450a86a8cb…
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