This study proposes a dual-incentive mechanism that integrates model training performance with worker satisfaction, encouraging providers to allocate more resources to safety knowledge recommendation and shows that the dual-incentive mechanism yields stronger incentive intensity, higher provider effort, and greater requester benefits versus single-model training incentives, while remaining robust across cost and risk variations.

Shuai Zhang, Botao Zhong, Samad M. E. Sepasgozar et al. · Journal of construction engi... · 0 citations

#federated learning Oct 2026

Dual prototypes for heterogeneous Federated Learning: A degradation perspective

Wen Ou, Tianchi Liao, Chuan Chen · Engineering applications of... · 0 citations

#federated learning Open access Oct 2026

Secure and efficient Federated Learning using sketches and Fully Homomorphic Encryption

E. M. Sarmento, J. Batista, J. J. Bastos et al. · Journal of Network and Compu... · 0 citations

#federated learning Oct 2026

Data-distribution-driven clustered federated learning with dual-perturbation

B.A. Jiang, Shengnan Zhao, Jiyang Chen et al. · Knowledge-Based Systems · 1 citation

#federated learning Oct 2026

Federated few-shot learning with explainable prototype representations for tuberculosis detection in chest X-rays

Safa Ben Atitallah, Maha Driss, Wadii Boulila et al. · Information Sciences · 0 citations

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Google Research Blog · research.google Sep 3, 2026

federated learning

A privacy-aware federated learning framework for collaborative healthcare IoT security

ZTE-FL: Dual-layer trust aggregation for secure federated learning in IIoT

A federated class-incremental learning framework with dynamic client participation and evolution for machine fault diagnosis

Fed-LPAR: Hierarchical personalization and adaptive regularization for differentially private federated learning

Addressing class imbalance in federated learning: A perspective of catastrophic forgetting

Attack-agnostic robust decentralized federated learning

Shuffle-model-based federated learning for communication cost–privacy–utility trade-offs

Toward a Federated Cross-Organizational Construction Safety Knowledge Recommendation: A Dual-Incentive Mechanism Integrating Worker Satisfaction

Dual prototypes for heterogeneous Federated Learning: A degradation perspective

Secure and efficient Federated Learning using sketches and Fully Homomorphic Encryption

Data-distribution-driven clustered federated learning with dual-perturbation

Federated few-shot learning with explainable prototype representations for tuberculosis detection in chest X-rays

From tech blogs

Transfer learning for genomic prediction in underrepresented populations

Mapping global methane emissions from space with deep learning

Looking beyond natural sequences

Generating scenarios for extreme events, without extreme data

When AI art has no author: Study finds generated images often can’t be traced to training data

What We Learned by Reproducing 2,200 papers from ICML