Enhancing Security and Privacy in Federated Learning using Low-Dimensional Update Representation and Proximity-Based Defense
Fuente:
arXiv
Saved in:
| Main Authors: | Li, Wenjie, Fan, Kai, Zhang, Jingyuan, Li, Hui, Lim, Wei Yang Bryan, Yang, Qiang |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning
by: Zhang, Fuyao, et al.
Published: (2025)
by: Zhang, Fuyao, et al.
Published: (2025)
A Learning-Based Attack Framework to Break SOTA Poisoning Defenses in Federated Learning
by: Yang, Yuxin, et al.
Published: (2024)
by: Yang, Yuxin, et al.
Published: (2024)
Distributed Backdoor Attacks on Federated Graph Learning and Certified Defenses
by: Yang, Yuxin, et al.
Published: (2024)
by: Yang, Yuxin, et al.
Published: (2024)
Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning
by: Xu, Runhua, et al.
Published: (2025)
by: Xu, Runhua, et al.
Published: (2025)
A Game-theoretic Framework for Privacy-preserving Federated Learning
by: Zhang, Xiaojin, et al.
Published: (2023)
by: Zhang, Xiaojin, et al.
Published: (2023)
Differential Privacy Personalized Federated Learning Based on Dynamically Sparsified Client Updates
by: Wang, Chuanyin, et al.
Published: (2025)
by: Wang, Chuanyin, et al.
Published: (2025)
Efficient Byzantine-Robust and Provably Privacy-Preserving Federated Learning
by: Nie, Chenfei, et al.
Published: (2024)
by: Nie, Chenfei, et al.
Published: (2024)
Deciphering the Interplay between Attack and Protection Complexity in Privacy-Preserving Federated Learning
by: Zhang, Xiaojin, et al.
Published: (2025)
by: Zhang, Xiaojin, et al.
Published: (2025)
Decentralized Federated Learning: A Survey on Security and Privacy
by: Hallaji, Ehsan, et al.
Published: (2024)
by: Hallaji, Ehsan, et al.
Published: (2024)
Enhancing Privacy of Spatiotemporal Federated Learning against Gradient Inversion Attacks
by: Zheng, Lele, et al.
Published: (2024)
by: Zheng, Lele, et al.
Published: (2024)
System Password Security: Attack and Defense Mechanisms
by: Shi, Chaofang, et al.
Published: (2025)
by: Shi, Chaofang, et al.
Published: (2025)
Enhancing Security Using Random Binary Weights in Privacy-Preserving Federated Learning
by: Sawada, Hiroto, et al.
Published: (2024)
by: Sawada, Hiroto, et al.
Published: (2024)
Oblivionis: A Lightweight Learning and Unlearning Framework for Federated Large Language Models
by: Zhang, Fuyao, et al.
Published: (2025)
by: Zhang, Fuyao, et al.
Published: (2025)
A Novel Federated Learning-Based IDS for Enhancing UAVs Privacy and Security
by: Ceviz, Ozlem, et al.
Published: (2023)
by: Ceviz, Ozlem, et al.
Published: (2023)
FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMs
by: Han, Shanshan, et al.
Published: (2023)
by: Han, Shanshan, et al.
Published: (2023)
Uncovering Security Threats and Architecting Defenses in Autonomous Agents: A Case Study of OpenClaw
by: Ying, Zonghao, et al.
Published: (2026)
by: Ying, Zonghao, et al.
Published: (2026)
On the (In-)Security of the Shuffling Defense in the Transformer Secure Inference
by: Li, Zhengyi, et al.
Published: (2026)
by: Li, Zhengyi, et al.
Published: (2026)
Defensible Design for OpenClaw: Securing Autonomous Tool-Invoking Agents
by: Li, Zongwei, et al.
Published: (2026)
by: Li, Zongwei, et al.
Published: (2026)
TAPFed: Threshold Secure Aggregation for Privacy-Preserving Federated Learning
by: Xu, Runhua, et al.
Published: (2025)
by: Xu, Runhua, et al.
Published: (2025)
Hyperparameter Optimization for SecureBoost via Constrained Multi-Objective Federated Learning
by: Kang, Yan, et al.
Published: (2024)
by: Kang, Yan, et al.
Published: (2024)
FLSSM: A Federated Learning Storage Security Model with Homomorphic Encryption
by: Li, Yang, et al.
Published: (2025)
by: Li, Yang, et al.
Published: (2025)
Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents
by: Zhang, Hanrong, et al.
Published: (2024)
by: Zhang, Hanrong, et al.
Published: (2024)
FedAdOb: Privacy-Preserving Federated Deep Learning with Adaptive Obfuscation
by: Gu, Hanlin, et al.
Published: (2024)
by: Gu, Hanlin, et al.
Published: (2024)
Towards Securing IIoT: An Innovative Privacy-Preserving Anomaly Detector Based on Federated Learning
by: Poorazad, Samira Kamali, et al.
Published: (2026)
by: Poorazad, Samira Kamali, et al.
Published: (2026)
Efficient Secure Aggregation for Privacy-Preserving Federated Machine Learning
by: Behnia, Rouzbeh, et al.
Published: (2023)
by: Behnia, Rouzbeh, et al.
Published: (2023)
Towards Achieving Near-optimal Utility for Privacy-Preserving Federated Learning via Data Generation and Parameter Distortion
by: Zhang, Xiaojin, et al.
Published: (2023)
by: Zhang, Xiaojin, et al.
Published: (2023)
Security Analysis of ChatGPT: Threats and Privacy Risks
by: Xiang, Yushan, et al.
Published: (2025)
by: Xiang, Yushan, et al.
Published: (2025)
Membership Inference Attacks and Defenses in Federated Learning: A Survey
by: Bai, Li, et al.
Published: (2024)
by: Bai, Li, et al.
Published: (2024)
Preserving Privacy and Security in Federated Learning
by: Nguyen, Truc, et al.
Published: (2022)
by: Nguyen, Truc, et al.
Published: (2022)
MultiPriv: Benchmarking Individual-Level Privacy Reasoning in Vision-Language Models
by: Sun, Xiongtao, et al.
Published: (2025)
by: Sun, Xiongtao, et al.
Published: (2025)
AntiFLipper: A Secure and Efficient Defense Against Label-Flipping Attacks in Federated Learning
by: Rahman, Aashnan, et al.
Published: (2025)
by: Rahman, Aashnan, et al.
Published: (2025)
Privacy in Large Language Models: Attacks, Defenses and Future Directions
by: Li, Haoran, et al.
Published: (2023)
by: Li, Haoran, et al.
Published: (2023)
Efficiently Achieving Secure Model Training and Secure Aggregation to Ensure Bidirectional Privacy-Preservation in Federated Learning
by: Yang, Xue, et al.
Published: (2024)
by: Yang, Xue, et al.
Published: (2024)
Enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare Applications
by: Taiello, Riccardo, et al.
Published: (2024)
by: Taiello, Riccardo, et al.
Published: (2024)
Trustworthy Federated Learning: Privacy, Security, and Beyond
by: Chen, Chunlu, et al.
Published: (2024)
by: Chen, Chunlu, et al.
Published: (2024)
FedGMark: Certifiably Robust Watermarking for Federated Graph Learning
by: Yang, Yuxin, et al.
Published: (2024)
by: Yang, Yuxin, et al.
Published: (2024)
Uncovering Attacks and Defenses in Secure Aggregation for Federated Deep Learning
by: Zhang, Yiwei, et al.
Published: (2024)
by: Zhang, Yiwei, et al.
Published: (2024)
DualTAP: A Dual-Task Adversarial Protector for Mobile MLLM Agents
by: Zhang, Fuyao, et al.
Published: (2025)
by: Zhang, Fuyao, et al.
Published: (2025)
Backdoor-Powered Prompt Injection Attacks Nullify Defense Methods
by: Chen, Yulin, et al.
Published: (2025)
by: Chen, Yulin, et al.
Published: (2025)
SecurityLingua: Efficient Defense of LLM Jailbreak Attacks via Security-Aware Prompt Compression
by: Li, Yucheng, et al.
Published: (2025)
by: Li, Yucheng, et al.
Published: (2025)
Similar Items
-
Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning
by: Zhang, Fuyao, et al.
Published: (2025) -
A Learning-Based Attack Framework to Break SOTA Poisoning Defenses in Federated Learning
by: Yang, Yuxin, et al.
Published: (2024) -
Distributed Backdoor Attacks on Federated Graph Learning and Certified Defenses
by: Yang, Yuxin, et al.
Published: (2024) -
Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning
by: Xu, Runhua, et al.
Published: (2025) -
A Game-theoretic Framework for Privacy-preserving Federated Learning
by: Zhang, Xiaojin, et al.
Published: (2023)