Imagen de Portada

SODA: Protecting Proprietary Information in On-Device Machine Learning Models

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Atrey, Akanksha, Sinha, Ritwik, Mitra, Saayan, Shenoy, Prashant
Formato:	Preprint
Publicado:	2023
Materias:	Machine Learning Cryptography and Security Distributed, Parallel, and Cluster Computing
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Protecting Confidentiality, Privacy and Integrity in Collaborative Learning
por: Chen, Dong, et al.
Publicado: (2024)

Privacy Preserving Machine Learning Model Personalization through Federated Personalized Learning
por: Hosain, Md. Tanzib, et al.
Publicado: (2025)

Byzantine-Robust Decentralized Federated Learning
por: Fang, Minghong, et al.
Publicado: (2024)

Balancing Privacy, Robustness, and Efficiency in Machine Learning
por: Allouah, Youssef, et al.
Publicado: (2023)

DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation
por: Xu, Jie, et al.
Publicado: (2024)

Update Selective Parameters: Federated Machine Unlearning Based on Model Explanation
por: Xu, Heng, et al.
Publicado: (2024)

Prada: Black-Box LLM Adaptation with Private Data on Resource-Constrained Devices
por: Wang, Ziyao, et al.
Publicado: (2025)

Binary Linear Tree Commitment-based Ownership Protection for Distributed Machine Learning
por: Xie, Tianxiu, et al.
Publicado: (2024)

Privacy-Enhanced Training-as-a-Service for On-Device Intelligence: Concept, Architectural Scheme, and Open Problems
por: Wu, Zhiyuan, et al.
Publicado: (2024)

Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach
por: Tan, Qi, et al.
Publicado: (2024)

Foundation Models in Federated Learning: Assessing Backdoor Vulnerabilities
por: Li, Xi, et al.
Publicado: (2024)

Identify Backdoored Model in Federated Learning via Individual Unlearning
por: Xu, Jiahao, et al.
Publicado: (2024)

Efficient Language Model Architectures for Differentially Private Federated Learning
por: Ro, Jae Hun, et al.
Publicado: (2024)

FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning
por: Zhang, Jianqing, et al.
Publicado: (2024)

FedBaF: Federated Learning Aggregation Biased by a Foundation Model
por: Park, Jong-Ik, et al.
Publicado: (2024)

On-Chain Decentralized Learning and Cost-Effective Inference for DeFi Attack Mitigation
por: Alhaidari, Abdulrahman, et al.
Publicado: (2025)

Achieving Byzantine-Resilient Federated Learning via Layer-Adaptive Sparsified Model Aggregation
por: Xu, Jiahao, et al.
Publicado: (2024)

Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning
por: Zhang, Hangtao, et al.
Publicado: (2023)

Towards Trustworthy Federated Learning
por: Basharat, Alina, et al.
Publicado: (2025)

A Survey on Decentralized Federated Learning
por: Gabrielli, Edoardo, et al.
Publicado: (2023)

Personalized Federated Learning via Stacking
por: Cantu-Cervini, Emilio
Publicado: (2024)

Differentially Private Clustered Federated Learning
por: Malekmohammadi, Saber, et al.
Publicado: (2024)

Provably Robust Federated Reinforcement Learning
por: Fang, Minghong, et al.
Publicado: (2025)

Anomalous Client Detection in Federated Learning
por: Thakur, Dipanwita, et al.
Publicado: (2024)

Protecting Split Learning by Potential Energy Loss
por: Zheng, Fei, et al.
Publicado: (2022)

UNIDEAL: Curriculum Knowledge Distillation Federated Learning
por: Yang, Yuwen, et al.
Publicado: (2023)

Toward Malicious Clients Detection in Federated Learning
por: Dou, Zhihao, et al.
Publicado: (2025)

SecureAFL: Secure Asynchronous Federated Learning
por: Gao, Anjun, et al.
Publicado: (2026)

Towards Trustworthy Federated Learning with Untrusted Participants
por: Allouah, Youssef, et al.
Publicado: (2025)

End-to-End Verifiable Decentralized Federated Learning
por: Lee, Chaehyeon, et al.
Publicado: (2024)

Differentially-Private Multi-Tier Federated Learning
por: Chen, Evan, et al.
Publicado: (2024)

A Survey for Federated Learning Evaluations: Goals and Measures
por: Chai, Di, et al.
Publicado: (2023)

SecureSplit: Mitigating Backdoor Attacks in Split Learning
por: Dou, Zhihao, et al.
Publicado: (2026)

Age Aware Scheduling for Differentially-Private Federated Learning
por: Lin, Kuan-Yu, et al.
Publicado: (2024)

Poisoning with A Pill: Circumventing Detection in Federated Learning
por: Guo, Hanxi, et al.
Publicado: (2024)

Communication-Efficient and Privacy-Preserving Decentralized Meta-Learning
por: Yang, Hansi, et al.
Publicado: (2024)

Differentially Private Online Federated Learning with Correlated Noise
por: Zhang, Jiaojiao, et al.
Publicado: (2024)

AdFL: In-Browser Federated Learning for Online Advertisement
por: Alemari, Ahmad, et al.
Publicado: (2026)

No Vandalism: Privacy-Preserving and Byzantine-Robust Federated Learning
por: Xing, Zhibo, et al.
Publicado: (2024)

Federated Graph Learning with Adaptive Importance-based Sampling
por: Li, Anran, et al.
Publicado: (2024)