What is recorded here?
Connected encrypted client uploads to predeclared access policies, attested TEE workloads and a stateful key-management ledger; later training deployment added distributed Python execution and controlled recovery while optimizing device participation.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Bundles 2024 architecture and 2026 training-system evolution with separate author sets. Unlike classical FedAvg, encrypted training examples may leave devices. Confidentiality/attestation and privacy accounting depend on TEE hardware, trusted code, policy and state continuity; side channels and hardware vulnerabilities remain outside stated protections. Open-source inspectability is not a formal proof that every workload implements DP. Arbitrary Python is responsible for anonymizing released results. Deployment/utility claims are provider-reported, not independently replayed.
Read the original work
- Confidential Federated Computations
- Toward provably private learning from federated data
- Google Research: Toward provably private learning from federated data
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
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People and systems
Hubert Eichner; Daniel Ramage; Kallista Bonawitz; Dzmitry Huba; Tiziano Santoro; Brett McLarnon; Timon Van Overveldt; Nova Fallen; Peter Kairouz; Albert Cheu; Katharine Daly; Adria Gascon; Marco Gruteser; Brendan McMahan; Yu Xiao; Zachary Garrett; Jianpeng Hou; Arun Ganesh; Yanxiang Zhang; Noriyuki Takahashi; Haicheng Sun; Yuanbo Zhang
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Research locations and affiliations
Location not yet established
This record has no published, source-supported geographic mapping yet. Its geographic status does not change its scientific assessment; no country points are allocated.
English catalogue narrative
Confidential federated computation: policy-bound TEE processing and training
Connected encrypted client uploads to predeclared access policies, attested TEE workloads and a stateful key-management ledger; later training deployment added distributed Python execution and controlled recovery while optimizing device participation.
Scientific scope
Bundles 2024 architecture and 2026 training-system evolution with separate author sets. Unlike classical FedAvg, encrypted training examples may leave devices. Confidentiality/attestation and privacy accounting depend on TEE hardware, trusted code, policy and state continuity; side channels and hardware vulnerabilities remain outside stated protections. Open-source inspectability is not a formal proof that every workload implements DP. Arbitrary Python is responsible for anonymizing released results. Deployment/utility claims are provider-reported, not independently replayed.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Hubert Eichner; Daniel Ramage; Kallista Bonawitz; Dzmitry Huba; Tiziano Santoro; Brett McLarnon; Timon Van Overveldt; Nova Fallen; Peter Kairouz; Albert Cheu; Katharine Daly; Adria Gascon; Marco Gruteser; Brendan McMahan; Yu Xiao; Zachary Garrett; Jianpeng Hou; Arun Ganesh; Yanxiang Zhang; Noriyuki Takahashi; Haicheng Sun; Yuanbo Zhang
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.