Model Inversion in Split Learning for Personalized LLMs: New Insights from Information Bottleneck Theory
Fuente:
arXiv
Guardado en:
| Autores principales: | Shu, Yunmeng, Li, Shaofeng, Dong, Tian, Meng, Yan, Zhu, Haojin |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Step-wise Adaptive Integration of Supervised Fine-tuning and Reinforcement Learning for Task-Specific LLMs
por: Chen, Jack, et al.
Publicado: (2025)
por: Chen, Jack, et al.
Publicado: (2025)
Depth Gives a False Sense of Privacy: LLM Internal States Inversion
por: Dong, Tian, et al.
Publicado: (2025)
por: Dong, Tian, et al.
Publicado: (2025)
A Generalized Information Bottleneck Theory of Deep Learning
por: Westphal, Charles, et al.
Publicado: (2025)
por: Westphal, Charles, et al.
Publicado: (2025)
Is the Information Bottleneck Robust Enough? Towards Label-Noise Resistant Information Bottleneck Learning
por: Huang, Yi, et al.
Publicado: (2025)
por: Huang, Yi, et al.
Publicado: (2025)
Distributional Reinforcement Learning with Information Bottleneck for Uncertainty-Aware DRAM Equalization
por: Usama, Muhammad, et al.
Publicado: (2026)
por: Usama, Muhammad, et al.
Publicado: (2026)
Concepts' Information Bottleneck Models
por: Galliamov, Karim, et al.
Publicado: (2026)
por: Galliamov, Karim, et al.
Publicado: (2026)
Optimizing EEG Graph Structure for Seizure Detection: An Information Bottleneck and Self-Supervised Learning Approach
por: Li, Lincan, et al.
Publicado: (2026)
por: Li, Lincan, et al.
Publicado: (2026)
Learning Optimal Multimodal Information Bottleneck Representations
por: Wu, Qilong, et al.
Publicado: (2025)
por: Wu, Qilong, et al.
Publicado: (2025)
Controllable Concept Bottleneck Models
por: Lin, Hongbin, et al.
Publicado: (2026)
por: Lin, Hongbin, et al.
Publicado: (2026)
IBCircuit: Towards Holistic Circuit Discovery with Information Bottleneck
por: Bian, Tian, et al.
Publicado: (2026)
por: Bian, Tian, et al.
Publicado: (2026)
Discrete Curvature Graph Information Bottleneck
por: Fu, Xingcheng, et al.
Publicado: (2024)
por: Fu, Xingcheng, et al.
Publicado: (2024)
Separate Aggregation of Split Network for Personalized Federated Learning
por: Kang, Yunseok, et al.
Publicado: (2026)
por: Kang, Yunseok, et al.
Publicado: (2026)
EvoCoT: Overcoming the Exploration Bottleneck in Reinforcement Learning
por: Liu, Huanyu, et al.
Publicado: (2025)
por: Liu, Huanyu, et al.
Publicado: (2025)
IBNorm: Information-Bottleneck Inspired Normalization for Representation Learning
por: Zou, Xiandong, et al.
Publicado: (2025)
por: Zou, Xiandong, et al.
Publicado: (2025)
Variational Geometric Information Bottleneck: Learning the Shape of Understanding
por: Katende, Ronald
Publicado: (2025)
por: Katende, Ronald
Publicado: (2025)
Trustworthy Representation Learning via Information Funnels and Bottlenecks
por: de Freitas, João Machado, et al.
Publicado: (2022)
por: de Freitas, João Machado, et al.
Publicado: (2022)
Distributed Information Bottleneck Theory for Multi-Modal Task-Aware Semantic Communication
por: Zhou, Yujie, et al.
Publicado: (2025)
por: Zhou, Yujie, et al.
Publicado: (2025)
ASFL: An Adaptive Model Splitting and Resource Allocation Framework for Split Federated Learning
por: Meng, Chuiyang, et al.
Publicado: (2026)
por: Meng, Chuiyang, et al.
Publicado: (2026)
TimeX++: Learning Time-Series Explanations with Information Bottleneck
por: Liu, Zichuan, et al.
Publicado: (2024)
por: Liu, Zichuan, et al.
Publicado: (2024)
There Was Never a Bottleneck in Concept Bottleneck Models
por: Almudévar, Antonio, et al.
Publicado: (2025)
por: Almudévar, Antonio, et al.
Publicado: (2025)
LINC: Decoupling Local Consequence Scoring from Hidden Matching in Constructive Neural Routing
por: Qin, Shaofeng, et al.
Publicado: (2026)
por: Qin, Shaofeng, et al.
Publicado: (2026)
Model Inversion with Layer-Specific Modeling and Alignment for Data-Free Continual Learning
por: Tong, Ruilin, et al.
Publicado: (2025)
por: Tong, Ruilin, et al.
Publicado: (2025)
Federated Learning with Enhanced Privacy via Model Splitting and Random Client Participation
por: Li, Yiwei, et al.
Publicado: (2025)
por: Li, Yiwei, et al.
Publicado: (2025)
Information Must Flow: Recursive Bootstrapping for Information Bottleneck in Optimal Transport
por: Li, Xin
Publicado: (2025)
por: Li, Xin
Publicado: (2025)
A Distance Metric Learning Model Based On Variational Information Bottleneck
por: Zhang, YaoDan, et al.
Publicado: (2024)
por: Zhang, YaoDan, et al.
Publicado: (2024)
Matryoshka Concept Bottleneck Models
por: Chen, Ziye, et al.
Publicado: (2026)
por: Chen, Ziye, et al.
Publicado: (2026)
LDC-MTL: Balancing Multi-Task Learning through Scalable Loss Discrepancy Control
por: Xiao, Peiyao, et al.
Publicado: (2025)
por: Xiao, Peiyao, et al.
Publicado: (2025)
Dynamic Graph Information Bottleneck
por: Yuan, Haonan, et al.
Publicado: (2024)
por: Yuan, Haonan, et al.
Publicado: (2024)
IBEX: Information-Bottleneck-EXplored Coarse-to-Fine Molecular Generation under Limited Data
por: Xu, Dong, et al.
Publicado: (2025)
por: Xu, Dong, et al.
Publicado: (2025)
Near-Optimal Sample Complexity for Iterated CVaR Reinforcement Learning with a Generative Model
por: Deng, Zilong, et al.
Publicado: (2025)
por: Deng, Zilong, et al.
Publicado: (2025)
Learning Fair Graph Representations with Multi-view Information Bottleneck
por: Liu, Chuxun, et al.
Publicado: (2025)
por: Liu, Chuxun, et al.
Publicado: (2025)
Constrained Reinforcement Learning Under Model Mismatch
por: Sun, Zhongchang, et al.
Publicado: (2024)
por: Sun, Zhongchang, et al.
Publicado: (2024)
Differentiable Information Bottleneck for Deterministic Multi-view Clustering
por: Yan, Xiaoqiang, et al.
Publicado: (2024)
por: Yan, Xiaoqiang, et al.
Publicado: (2024)
Breaking Training Bottlenecks: Effective and Stable Reinforcement Learning for Coding Models
por: Li, Zongqian, et al.
Publicado: (2026)
por: Li, Zongqian, et al.
Publicado: (2026)
AuroRA: Breaking Low-Rank Bottleneck of LoRA with Nonlinear Mapping
por: Dong, Haonan, et al.
Publicado: (2025)
por: Dong, Haonan, et al.
Publicado: (2025)
Debiasing Graph Representation Learning based on Information Bottleneck
por: Zhang, Ziyi, et al.
Publicado: (2024)
por: Zhang, Ziyi, et al.
Publicado: (2024)
Multimodal Information Bottleneck for Deep Reinforcement Learning with Multiple Sensors
por: You, Bang, et al.
Publicado: (2024)
por: You, Bang, et al.
Publicado: (2024)
Learning Concept Bottleneck Models from Mechanistic Explanations
por: De Santis, Antonio, et al.
Publicado: (2026)
por: De Santis, Antonio, et al.
Publicado: (2026)
Model-Free Robust Reinforcement Learning with Sample Complexity Analysis
por: Wang, Yudan, et al.
Publicado: (2024)
por: Wang, Yudan, et al.
Publicado: (2024)
Refining the Information Bottleneck via Adversarial Information Separation
por: Ning, Shuai, et al.
Publicado: (2026)
por: Ning, Shuai, et al.
Publicado: (2026)
Ejemplares similares
-
Step-wise Adaptive Integration of Supervised Fine-tuning and Reinforcement Learning for Task-Specific LLMs
por: Chen, Jack, et al.
Publicado: (2025) -
Depth Gives a False Sense of Privacy: LLM Internal States Inversion
por: Dong, Tian, et al.
Publicado: (2025) -
A Generalized Information Bottleneck Theory of Deep Learning
por: Westphal, Charles, et al.
Publicado: (2025) -
Is the Information Bottleneck Robust Enough? Towards Label-Noise Resistant Information Bottleneck Learning
por: Huang, Yi, et al.
Publicado: (2025) -
Distributional Reinforcement Learning with Information Bottleneck for Uncertainty-Aware DRAM Equalization
por: Usama, Muhammad, et al.
Publicado: (2026)