Forward Learning with Differential Privacy
Fuente:
arXiv
Saved in:
| Main Authors: | Feng, Mingqian, Zhang, Zeliang, Jiang, Jinyang, Peng, Yijie, Xu, Chenliang |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
FLOPS: Forward Learning with OPtimal Sampling
by: Ren, Tao, et al.
Published: (2024)
by: Ren, Tao, et al.
Published: (2024)
Approximated Likelihood Ratio: A Forward-Only and Parallel Framework for Boosting Neural Network Training
by: Zhang, Zeliang, et al.
Published: (2024)
by: Zhang, Zeliang, et al.
Published: (2024)
Forward Learning for Gradient-based Black-box Saliency Map Generation
by: Zhang, Zeliang, et al.
Published: (2024)
by: Zhang, Zeliang, et al.
Published: (2024)
Closing the Loop: Coordinating Inventory and Recommendation via Deep Reinforcement Learning on Multiple Timescales
by: Jiang, Jinyang, et al.
Published: (2025)
by: Jiang, Jinyang, et al.
Published: (2025)
CoNNect: Connectivity-Based Regularization for Structural Pruning
by: Franssen, Christian, et al.
Published: (2025)
by: Franssen, Christian, et al.
Published: (2025)
Do More Details Always Introduce More Hallucinations in LVLM-based Image Captioning?
by: Feng, Mingqian, et al.
Published: (2024)
by: Feng, Mingqian, et al.
Published: (2024)
Discover and Mitigate Multiple Biased Subgroups in Image Classifiers
by: Zhang, Zeliang, et al.
Published: (2024)
by: Zhang, Zeliang, et al.
Published: (2024)
Can CLIP Count Stars? An Empirical Study on Quantity Bias in CLIP
by: Zhang, Zeliang, et al.
Published: (2024)
by: Zhang, Zeliang, et al.
Published: (2024)
Diversifying the Expert Knowledge for Task-Agnostic Pruning in Sparse Mixture-of-Experts
by: Zhang, Zeliang, et al.
Published: (2024)
by: Zhang, Zeliang, et al.
Published: (2024)
Stochastic Approximation Methods for Distortion Risk Measure Optimization
by: Jiang, Jinyang, et al.
Published: (2025)
by: Jiang, Jinyang, et al.
Published: (2025)
Omni-Masked Gradient Descent: Memory-Efficient Optimization via Mask Traversal with Improved Convergence
by: Yang, Hui, et al.
Published: (2026)
by: Yang, Hui, et al.
Published: (2026)
Training Large Reasoning Models Efficiently via Progressive Thought Encoding
by: Zhang, Zeliang, et al.
Published: (2026)
by: Zhang, Zeliang, et al.
Published: (2026)
The Limits of Differential Privacy in Online Learning
by: Li, Bo, et al.
Published: (2024)
by: Li, Bo, et al.
Published: (2024)
Will the Inclusion of Generated Data Amplify Bias Across Generations in Future Image Classification Models?
by: Zhang, Zeliang, et al.
Published: (2024)
by: Zhang, Zeliang, et al.
Published: (2024)
Deep Reinforcement Learning for Solving Management Problems: Towards A Large Management Mode
by: Jiang, Jinyang, et al.
Published: (2024)
by: Jiang, Jinyang, et al.
Published: (2024)
Reverse-Complement Consistency for DNA Language Models
by: Ma, Mingqian
Published: (2025)
by: Ma, Mingqian
Published: (2025)
Covariance-Aware Goodness for Scalable Forward-Forward Learning
by: Jiang, Xiaoyi, et al.
Published: (2026)
by: Jiang, Xiaoyi, et al.
Published: (2026)
Federated Transfer Learning with Differential Privacy
by: Li, Mengchu, et al.
Published: (2024)
by: Li, Mengchu, et al.
Published: (2024)
Tackling Privacy Heterogeneity in Differentially Private Federated Learning
by: Xu, Ruichen, et al.
Published: (2026)
by: Xu, Ruichen, et al.
Published: (2026)
Optimizing Communication and Device Clustering for Clustered Federated Learning with Differential Privacy
by: Wei, Dongyu, et al.
Published: (2025)
by: Wei, Dongyu, et al.
Published: (2025)
On Cold Posteriors of Probabilistic Neural Networks: Understanding the Cold Posterior Effect and A New Way to Learn Cold Posteriors with Tight Generalization Guarantees
by: Zhang, Yijie
Published: (2024)
by: Zhang, Yijie
Published: (2024)
Dual-Agent Deep Reinforcement Learning for Dynamic Pricing and Replenishment
by: Zheng, Yi, et al.
Published: (2024)
by: Zheng, Yi, et al.
Published: (2024)
Learning with User-Level Local Differential Privacy
by: Zhao, Puning, et al.
Published: (2024)
by: Zhao, Puning, et al.
Published: (2024)
Adversarial Signed Graph Learning with Differential Privacy
by: Ke, Haobin, et al.
Published: (2025)
by: Ke, Haobin, et al.
Published: (2025)
Sample-Efficient "Clustering and Conquer" Procedures for Parallel Large-Scale Ranking and Selection
by: Zhang, Zishi, et al.
Published: (2024)
by: Zhang, Zishi, et al.
Published: (2024)
Moonwalk: Inverse-Forward Differentiation
by: Krylov, Dmitrii, et al.
Published: (2024)
by: Krylov, Dmitrii, et al.
Published: (2024)
CoSIFL: Collaborative Secure and Incentivized Federated Learning with Differential Privacy
by: Xie, Zhanhong, et al.
Published: (2025)
by: Xie, Zhanhong, et al.
Published: (2025)
A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy
by: Li, Xiang, et al.
Published: (2025)
by: Li, Xiang, et al.
Published: (2025)
Near-Optimal Reinforcement Learning with Shuffle Differential Privacy
by: Bai, Shaojie, et al.
Published: (2024)
by: Bai, Shaojie, et al.
Published: (2024)
Convergent Differential Privacy Analysis for General Federated Learning
by: Sun, Yan, et al.
Published: (2024)
by: Sun, Yan, et al.
Published: (2024)
Adaptive Spatial Goodness Encoding: Advancing and Scaling Forward-Forward Learning Without Backpropagation
by: Gong, Qingchun, et al.
Published: (2025)
by: Gong, Qingchun, et al.
Published: (2025)
Enhancing Learning with Label Differential Privacy by Vector Approximation
by: Zhao, Puning, et al.
Published: (2024)
by: Zhao, Puning, et al.
Published: (2024)
When Differential Privacy Meets Wireless Federated Learning: An Improved Analysis for Privacy and Convergence
by: Yaoling, Chen, et al.
Published: (2026)
by: Yaoling, Chen, et al.
Published: (2026)
Bayesian Physics-Informed Extreme Learning Machine for Forward and Inverse PDE Problems with Noisy Data
by: Liu, Xu, et al.
Published: (2022)
by: Liu, Xu, et al.
Published: (2022)
Second-Order Forward-Mode Automatic Differentiation for Optimization
by: Cobb, Adam D., et al.
Published: (2024)
by: Cobb, Adam D., et al.
Published: (2024)
XQSV: A Structurally Variable Network to Imitate Human Play in Xiangqi
by: Zhou, Chenliang
Published: (2024)
by: Zhou, Chenliang
Published: (2024)
Stochastic Forward-Forward Learning through Representational Dimensionality Compression
by: Zhu, Zhichao, et al.
Published: (2025)
by: Zhu, Zhichao, et al.
Published: (2025)
Federated Hypergraph Learning with Local Differential Privacy: Toward Privacy-Aware Hypergraph Structure Completion
by: Luo, Linfeng, et al.
Published: (2024)
by: Luo, Linfeng, et al.
Published: (2024)
A New Stochastic Approximation Method for Gradient-based Simulated Parameter Estimation
by: Li, Zehao, et al.
Published: (2025)
by: Li, Zehao, et al.
Published: (2025)
Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy
by: Lin, Yingyu, et al.
Published: (2023)
by: Lin, Yingyu, et al.
Published: (2023)
Similar Items
-
FLOPS: Forward Learning with OPtimal Sampling
by: Ren, Tao, et al.
Published: (2024) -
Approximated Likelihood Ratio: A Forward-Only and Parallel Framework for Boosting Neural Network Training
by: Zhang, Zeliang, et al.
Published: (2024) -
Forward Learning for Gradient-based Black-box Saliency Map Generation
by: Zhang, Zeliang, et al.
Published: (2024) -
Closing the Loop: Coordinating Inventory and Recommendation via Deep Reinforcement Learning on Multiple Timescales
by: Jiang, Jinyang, et al.
Published: (2025) -
CoNNect: Connectivity-Based Regularization for Structural Pruning
by: Franssen, Christian, et al.
Published: (2025)