Verified80% confidenceFactExact time
Federated learning was proposed by Google in 2016, keeping data local and uploading only model gradients
4
Sources
80%
Confidence
Long-term
Relevance
7/7/2026
First Seen
Sources
硬件级AI安全：机密计算如何实现性能与防护双赢
rss7/2/2026
Related Claims
VerifiedFederated learning is a distributed machine learning paradigm proposed by Google in 2017, allowing models to be trained on local devices without uploading raw data to a central server.79% similarVerifiedFederated learning was proposed by Google in 2016, originally to address privacy issues in model training on mobile devices.77% similarUnverifiedThe supervised contrastive learning framework SupCon was proposed by Google Research in 2020 and is a supervised extension of self-supervised contrastive learning.70% similarUnverified谷歌于2016年宣布将移动端索引作为首要标准68% similarUnverifiedGoogle provides enterprise Workspace users with the option to commit that their data will not be used for model training67% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/185148API
curl https://kongchang.com/api/v1/knowledge/claims/185148MCP
get_claim(id=185148)