Copertina

Personalized Federated Learning via Backbone Self-Distillation

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Wang, Pengju, Liu, Bochao, Zeng, Dan, Yan, Chenggang, Ge, Shiming
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Artificial Intelligence Cryptography and Security Computer Vision and Pattern Recognition
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Towards Personalized Federated Learning via Comprehensive Knowledge Distillation
di: Wang, Pengju, et al.
Pubblicazione: (2024)

Learning Differentially Private Diffusion Models via Stochastic Adversarial Distillation
di: Liu, Bochao, et al.
Pubblicazione: (2024)

Privacy-Preserving Student Learning with Differentially Private Data-Free Distillation
di: Liu, Bochao, et al.
Pubblicazione: (2024)

Learning Privacy-Preserving Student Networks via Discriminative-Generative Distillation
di: Ge, Shiming, et al.
Pubblicazione: (2024)

Federated Learning with Label-Masking Distillation
di: Lu, Jianghu, et al.
Pubblicazione: (2024)

Privacy-Preserving Model Transcription with Differentially Private Synthetic Distillation
di: Liu, Bochao, et al.
Pubblicazione: (2026)

Interpret the Predictions of Deep Networks via Re-Label Distillation
di: Hua, Yingying, et al.
Pubblicazione: (2024)

On the Importance of Backbone to the Adversarial Robustness of Object Detectors
di: Li, Xiao, et al.
Pubblicazione: (2023)

An Efficient and Multi-private Key Secure Aggregation for Federated Learning
di: Yang, Xue, et al.
Pubblicazione: (2023)

FedFG: Privacy-Preserving and Robust Federated Learning via Flow-Matching Generation
di: Wang, Ruiyang, et al.
Pubblicazione: (2026)

PrivFedTalk: Privacy-Aware Federated Diffusion with Identity-Stable Adapters for Personalized Talking-Head Generation
di: Mazumdar, Soumya, et al.
Pubblicazione: (2026)

Taught Well Learned Ill: Towards Distillation-conditional Backdoor Attack
di: Chen, Yukun, et al.
Pubblicazione: (2025)

Private Gradient Estimation is Useful for Generative Modeling
di: Liu, Bochao, et al.
Pubblicazione: (2023)

Beyond Traditional Threats: A Persistent Backdoor Attack on Federated Learning
di: Liu, Tao, et al.
Pubblicazione: (2024)

Poisoning Attacks on Federated Learning for Autonomous Driving
di: Garg, Sonakshi, et al.
Pubblicazione: (2024)

FedDefender: Backdoor Attack Defense in Federated Learning
di: Gill, Waris, et al.
Pubblicazione: (2023)

Federated Learning With Individualized Privacy Through Client Sampling
di: Lange, Lucas, et al.
Pubblicazione: (2025)

With Great Backbones Comes Great Adversarial Transferability
di: Arakelyan, Erik, et al.
Pubblicazione: (2025)

pfl-research: simulation framework for accelerating research in Private Federated Learning
di: Granqvist, Filip, et al.
Pubblicazione: (2024)

Non-Cooperative Backdoor Attacks in Federated Learning: A New Threat Landscape
di: Nguyen, Tuan, et al.
Pubblicazione: (2024)

Erasing Self-Supervised Learning Backdoor by Cluster Activation Masking
di: Qian, Shengsheng, et al.
Pubblicazione: (2023)

A Novel Defense Against Poisoning Attacks on Federated Learning: LayerCAM Augmented with Autoencoder
di: Zheng, Jingjing, et al.
Pubblicazione: (2024)

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need
di: Hong, Ziming, et al.
Pubblicazione: (2025)

BackWeak: Backdooring Knowledge Distillation Simply with Weak Triggers and Fine-tuning
di: Wang, Shanmin, et al.
Pubblicazione: (2025)

Rotation-Invariant Spherical Watermarking via Third-Order SO(3) Representation Coupling
di: Chen, Pengzhen, et al.
Pubblicazione: (2026)

Value-Aligned Prompt Moderation via Zero-Shot Agentic Rewriting for Safe Image Generation
di: Zhao, Xin, et al.
Pubblicazione: (2025)

Efficient Backdoor Defense in Multimodal Contrastive Learning: A Token-Level Unlearning Method for Mitigating Threats
di: Liu, Kuanrong, et al.
Pubblicazione: (2024)

Leave My Images Alone: Preventing Multi-Modal Large Language Models from Analyzing Images via Visual Prompt Injection
di: Shao, Zedian, et al.
Pubblicazione: (2026)

GIFT: Gradient-aware Immunization of diffusion models against malicious Fine-Tuning with safe concepts retention
di: Abdalla, Amro, et al.
Pubblicazione: (2025)

SafeRedir: Prompt Embedding Redirection for Robust Unlearning in Image Generation Models
di: Liu, Renyang, et al.
Pubblicazione: (2026)

GNSS/GPS Spoofing and Jamming Identification Using Machine Learning and Deep Learning
di: Ghanbarzade, Ali, et al.
Pubblicazione: (2025)

Scalable Secure Biometric Authentication without Auxiliary Identifiers
di: Bienstock, Alexander, et al.
Pubblicazione: (2026)

REFINE: Inversion-Free Backdoor Defense via Model Reprogramming
di: Chen, Yukun, et al.
Pubblicazione: (2025)

Exploring the Adversarial Frontier: Quantifying Robustness via Adversarial Hypervolume
di: Guo, Ping, et al.
Pubblicazione: (2024)

Purify Unlearnable Examples via Rate-Constrained Variational Autoencoders
di: Yu, Yi, et al.
Pubblicazione: (2024)

Backdoor Attacks on Deep Learning Face Detection
di: Roux, Quentin Le, et al.
Pubblicazione: (2025)

GLEAN: Generative Learning for Eliminating Adversarial Noise
di: Kim, Justin Lyu, et al.
Pubblicazione: (2024)

Improving Robustness to Model Inversion Attacks via Sparse Coding Architectures
di: Dibbo, Sayanton V., et al.
Pubblicazione: (2024)

Hypnopaedia-Aware Machine Unlearning via Psychometrics of Artificial Mental Imagery
di: Chang, Ching-Chun, et al.
Pubblicazione: (2024)

Verification of Neural Networks against Convolutional Perturbations via Parameterised Kernels
di: Brückner, Benedikt, et al.
Pubblicazione: (2024)