Privacy-Preserving Federated Learning with Consistency via Knowledge Distillation Using Conditional Generator
Fuente:
arXiv
Saved in:
| Main Authors: | Luo, Kangyang, Wang, Shuai, Li, Xiang, Lan, Yunshi, Gao, Ming, Shu, Jinlong |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
DFDG: Data-Free Dual-Generator Adversarial Distillation for One-Shot Federated Learning
by: Luo, Kangyang, et al.
Published: (2024)
by: Luo, Kangyang, et al.
Published: (2024)
FairGFL: Privacy-Preserving Fairness-Aware Federated Learning with Overlapping Subgraphs
by: Zhou, Zihao, et al.
Published: (2025)
by: Zhou, Zihao, et al.
Published: (2025)
Sentinel: Dynamic Knowledge Distillation for Personalized Federated Intrusion Detection in Heterogeneous IoT Networks
by: Singh, Gurpreet, et al.
Published: (2025)
by: Singh, Gurpreet, et al.
Published: (2025)
FedAL: Black-Box Federated Knowledge Distillation Enabled by Adversarial Learning
by: Han, Pengchao, et al.
Published: (2023)
by: Han, Pengchao, et al.
Published: (2023)
Privacy-Preserving Federated Heavy Hitter Analytics for Non-IID Data
by: Shao, Jiaqi, et al.
Published: (2023)
by: Shao, Jiaqi, et al.
Published: (2023)
Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions
by: Qin, Laiqiao, et al.
Published: (2024)
by: Qin, Laiqiao, et al.
Published: (2024)
DROP: Poison Dilution via Knowledge Distillation for Federated Learning
by: Syros, Georgios, et al.
Published: (2025)
by: Syros, Georgios, et al.
Published: (2025)
Low-Cost Privacy-Preserving Decentralized Learning
by: Biswas, Sayan, et al.
Published: (2024)
by: Biswas, Sayan, et al.
Published: (2024)
No Vandalism: Privacy-Preserving and Byzantine-Robust Federated Learning
by: Xing, Zhibo, et al.
Published: (2024)
by: Xing, Zhibo, et al.
Published: (2024)
Using Diffusion Models as Generative Replay in Continual Federated Learning -- What will Happen?
by: Mei, Yongsheng, et al.
Published: (2024)
by: Mei, Yongsheng, et al.
Published: (2024)
FedGAT: A Privacy-Preserving Federated Approximation Algorithm for Graph Attention Networks
by: Ambekar, Siddharth, et al.
Published: (2024)
by: Ambekar, Siddharth, et al.
Published: (2024)
Clustered Federated Learning with Hierarchical Knowledge Distillation
by: Ahmad, Sabtain, et al.
Published: (2025)
by: Ahmad, Sabtain, et al.
Published: (2025)
UNIDEAL: Curriculum Knowledge Distillation Federated Learning
by: Yang, Yuwen, et al.
Published: (2023)
by: Yang, Yuwen, et al.
Published: (2023)
Federated Learning in the Presence of Adversarial Client Unavailability
by: Su, Lili, et al.
Published: (2023)
by: Su, Lili, et al.
Published: (2023)
Practical Framework for Privacy-Preserving and Byzantine-robust Federated Learning
by: Zhang, Baolei, et al.
Published: (2025)
by: Zhang, Baolei, et al.
Published: (2025)
Homogenizing Non-IID datasets via In-Distribution Knowledge Distillation for Decentralized Learning
by: Ravikumar, Deepak, et al.
Published: (2023)
by: Ravikumar, Deepak, et al.
Published: (2023)
Experiences Building Enterprise-Level Privacy-Preserving Federated Learning to Power AI for Science
by: Li, Zilinghan, et al.
Published: (2025)
by: Li, Zilinghan, et al.
Published: (2025)
Privacy Preserving Machine Learning Model Personalization through Federated Personalized Learning
by: Hosain, Md. Tanzib, et al.
Published: (2025)
by: Hosain, Md. Tanzib, et al.
Published: (2025)
Brave: Byzantine-Resilient and Privacy-Preserving Peer-to-Peer Federated Learning
by: Xu, Zhangchen, et al.
Published: (2024)
by: Xu, Zhangchen, 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)
Privacy-Preserving Federated Learning: Integrating Zero-Knowledge Proofs in Scalable Distributed Architectures
by: Gupta, Divya
Published: (2026)
by: Gupta, Divya
Published: (2026)
Accelerating Privacy-Preserving Federated Learning in Large-Scale LEO Satellite Systems
by: Guo, Binquan, et al.
Published: (2025)
by: Guo, Binquan, et al.
Published: (2025)
Detect \& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning
by: Xhemrishi, Marvin, et al.
Published: (2025)
by: Xhemrishi, Marvin, et al.
Published: (2025)
A Lightweight Federated Learning Approach for Privacy-Preserving Botnet Detection in IoT
by: Mahmoud, Taha M., et al.
Published: (2025)
by: Mahmoud, Taha M., et al.
Published: (2025)
IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning
by: Riya, Farhin Farhad, et al.
Published: (2026)
by: Riya, Farhin Farhad, et al.
Published: (2026)
Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learning
by: Zhao, Xiyu, et al.
Published: (2025)
by: Zhao, Xiyu, et al.
Published: (2025)
FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation
by: Xia, Tong, et al.
Published: (2024)
by: Xia, Tong, et al.
Published: (2024)
Device Association and Resource Allocation for Hierarchical Split Federated Learning in Space-Air-Ground Integrated Network
by: Zhao, Haitao, et al.
Published: (2026)
by: Zhao, Haitao, et al.
Published: (2026)
FedRandom: Sampling Consistent and Accurate Contribution Values in Federated Learning
by: Geimer, Arno, et al.
Published: (2026)
by: Geimer, Arno, et al.
Published: (2026)
FEDBUD: Joint Incentive and Privacy Optimization for Resource-Constrained Federated Learning
by: Liu, Tao, et al.
Published: (2026)
by: Liu, Tao, et al.
Published: (2026)
ParaAegis: Parallel Protection for Flexible Privacy-preserved Federated Learning
by: Wu, Zihou, et al.
Published: (2025)
by: Wu, Zihou, et al.
Published: (2025)
From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challenges
by: Li, Qiongxiu, et al.
Published: (2025)
by: Li, Qiongxiu, et al.
Published: (2025)
Dual-Distilled Heterogeneous Federated Learning with Adaptive Margins for Trainable Global Prototypes
by: Siddika, Fatema, et al.
Published: (2025)
by: Siddika, Fatema, et al.
Published: (2025)
Stragglers Can Contribute More: Uncertainty-Aware Distillation for Asynchronous Federated Learning
by: Wang, Yujia, et al.
Published: (2025)
by: Wang, Yujia, et al.
Published: (2025)
Empowering Federated Learning with Implicit Gossiping: Mitigating Connection Unreliability Amidst Unknown and Arbitrary Dynamics
by: Xiang, Ming, et al.
Published: (2024)
by: Xiang, Ming, et al.
Published: (2024)
FLMarket: Enabling Privacy-preserved Pre-training Data Pricing for Federated Learning
by: Wen, Zhenyu, et al.
Published: (2024)
by: Wen, Zhenyu, et al.
Published: (2024)
FedTilt: Towards Multi-Level Fairness-Preserving and Robust Federated Learning
by: Zhang, Binghui, et al.
Published: (2025)
by: Zhang, Binghui, et al.
Published: (2025)
ADP-VRSGP: Decentralized Learning with Adaptive Differential Privacy via Variance-Reduced Stochastic Gradient Push
by: Wu, Xiaoming, et al.
Published: (2025)
by: Wu, Xiaoming, et al.
Published: (2025)
Knowledge-Driven Federated Graph Learning on Model Heterogeneity
by: Wu, Zhengyu, et al.
Published: (2025)
by: Wu, Zhengyu, et al.
Published: (2025)
Training Heterogeneous Client Models using Knowledge Distillation in Serverless Federated Learning
by: Chadha, Mohak, et al.
Published: (2024)
by: Chadha, Mohak, et al.
Published: (2024)
Similar Items
-
DFDG: Data-Free Dual-Generator Adversarial Distillation for One-Shot Federated Learning
by: Luo, Kangyang, et al.
Published: (2024) -
FairGFL: Privacy-Preserving Fairness-Aware Federated Learning with Overlapping Subgraphs
by: Zhou, Zihao, et al.
Published: (2025) -
Sentinel: Dynamic Knowledge Distillation for Personalized Federated Intrusion Detection in Heterogeneous IoT Networks
by: Singh, Gurpreet, et al.
Published: (2025) -
FedAL: Black-Box Federated Knowledge Distillation Enabled by Adversarial Learning
by: Han, Pengchao, et al.
Published: (2023) -
Privacy-Preserving Federated Heavy Hitter Analytics for Non-IID Data
by: Shao, Jiaqi, et al.
Published: (2023)