Privacy-Preserving AI

AntTech

Nov 13, 2024 · Artificial Intelligence

Nimbus: Secure and Efficient Two‑Party Inference for Transformers

The article introduces Nimbus, a novel two‑party privacy‑preserving inference framework for Transformer models that accelerates linear‑layer matrix multiplication and activation‑function evaluation through an outer‑product encoding and distribution‑aware polynomial approximation, achieving 2.7‑4.7× speedup over prior work while maintaining model accuracy.

Privacy-Preserving AITransformerscryptography

0 likes · 6 min read

Nimbus: Secure and Efficient Two‑Party Inference for Transformers

DataFunTalk

Nov 6, 2021 · Artificial Intelligence

Elastic Federated Learning Solution (EFLS): Project Overview, Architecture, and Technical Implementation

The article introduces Alibaba's Elastic Federated Learning Solution (EFLS), describing its business motivations, core functionalities, system architecture, sample‑set intersection, federated training pipeline, novel algorithms, product console, and future roadmap for privacy‑preserving advertising in large‑scale sparse scenarios.

Federated LearningFlinkPrivacy-Preserving AI

0 likes · 18 min read

Elastic Federated Learning Solution (EFLS): Project Overview, Architecture, and Technical Implementation

Alimama Tech

Oct 27, 2021 · Artificial Intelligence

Elastic Federated Learning Solution (EFLS): Architecture, Core Functions, and Technical Details

The Elastic Federated Learning Solution (EFLS) is Alibaba’s open‑source platform that enables privacy‑preserving vertical and horizontal federated learning for large‑scale sparse advertising, offering data‑intersection, high‑performance C++ training, a visual console, novel aggregation algorithms, and a roadmap toward multi‑party scaling and advanced encryption.

Elastic Federated LearningFederated LearningFlink

0 likes · 16 min read

Elastic Federated Learning Solution (EFLS): Architecture, Core Functions, and Technical Details

DataFunTalk

May 28, 2021 · Artificial Intelligence

JD's Open‑Source Federated Learning Solution 9N‑FL: Architecture, Features, Timeline, and Business Impact

This article introduces JD's open‑source federated learning platform 9N‑FL, explaining the data‑island problem, the fundamentals and classifications of federated learning, its four key features, the system’s layered architecture, development timeline, real‑world advertising use case results, and future enhancements.

9N-FLData SecurityFederated Learning

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JD's Open‑Source Federated Learning Solution 9N‑FL: Architecture, Features, Timeline, and Business Impact