Cover Image

On the Tradeoff between Privacy Preservation and Byzantine-Robustness in Decentralized Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ye, Haoxiang, Zhu, Heng, Ling, Qing
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Cryptography and Security Distributed, Parallel, and Cluster Computing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

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

Practical Framework for Privacy-Preserving and Byzantine-robust Federated Learning
by: Zhang, Baolei, et al.
Published: (2025)

Brave: Byzantine-Resilient and Privacy-Preserving Peer-to-Peer Federated Learning
by: Xu, Zhangchen, et al.
Published: (2024)

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

Noiseless Privacy-Preserving Decentralized Learning
by: Biswas, Sayan, et al.
Published: (2024)

On the Hardness of Decentralized Multi-Agent Policy Evaluation under Byzantine Attacks
by: Hairi, et al.
Published: (2024)

Lancelot: Towards Efficient and Privacy-Preserving Byzantine-Robust Federated Learning within Fully Homomorphic Encryption
by: Jiang, Siyang, et al.
Published: (2024)

DP2Guard: A Lightweight and Byzantine-Robust Privacy-Preserving Federated Learning Scheme for Industrial IoT
by: Han, Baofu, et al.
Published: (2025)

Decentralized Collaborative Learning Framework with External Privacy Leakage Analysis
by: Idé, Tsuyoshi, et al.
Published: (2024)

Generalization Error Analysis for Attack-Free and Byzantine-Resilient Decentralized Learning with Data Heterogeneity
by: Ye, Haoxiang, et al.
Published: (2025)

The Robustness of Spiking Neural Networks in Federated Learning with Compression Against Non-omniscient Byzantine Attacks
by: Nguyen, Manh V., et al.
Published: (2025)

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

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

Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging
by: Cyffers, Edwige, et al.
Published: (2022)

Detect \& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning
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)

IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning
by: Riya, Farhin Farhad, et al.
Published: (2026)

Enhancing Privacy in Decentralized Min-Max Optimization: A Differentially Private Approach
by: Quan, Yueyang, et al.
Published: (2025)

Differential Privacy Analysis of Decentralized Gossip Averaging under Varying Threat Models
by: Koskela, Antti, et al.
Published: (2025)

Byzantine-Resilient Zero-Order Optimization for Communication-Efficient Heterogeneous Federated Learning
by: Egger, Maximilian, et al.
Published: (2025)

Aggressive or Imperceptible, or Both: Network Pruning Assisted Hybrid Byzantines in Federated Learning
by: Ozfatura, Emre, et al.
Published: (2024)

Byzantine-Resilient Secure Aggregation for Federated Learning Without Privacy Compromises
by: Xia, Yue, et al.
Published: (2024)

DeZent: Decentralized z-Anonymity with Privacy-Preserving Coordination
by: Brunn, Carolin, et al.
Published: (2026)

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

D2M: A Decentralized, Privacy-Preserving, Incentive-Compatible Data Marketplace for Collaborative Learning
by: Srivastava, Yash, et al.
Published: (2025)

Dropout-Robust Mechanisms for Differentially Private and Fully Decentralized Mean Estimation
by: Sabater, César, et al.
Published: (2025)

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

The Privacy Power of Correlated Noise in Decentralized Learning
by: Allouah, Youssef, et al.
Published: (2024)

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

High Dimensional Distributed Gradient Descent with Arbitrary Number of Byzantine Attackers
by: Liu, Wenyu, et al.
Published: (2023)

Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectives
by: Wang, Linlin, et al.
Published: (2024)

DPBalance: Efficient and Fair Privacy Budget Scheduling for Federated Learning as a Service
by: Liu, Yu, et al.
Published: (2024)

FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive Models
by: Lee, Younghan, et al.
Published: (2024)

Do We Really Need to Design New Byzantine-robust Aggregation Rules?
by: Fang, Minghong, et al.
Published: (2025)

Secure, Verifiable, and Scalable Multi-Client Data Sharing via Consensus-Based Privacy-Preserving Data Distribution
by: Panth, Prajwal, et al.
Published: (2026)

Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance
by: Wei, Wenqi, et al.
Published: (2024)

SmartphoneDemocracy: Privacy-Preserving E-Voting on Decentralized Infrastructure using Novel European Identity
by: Jóźwik, Michał, et al.
Published: (2025)

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

Improving LoRA in Privacy-preserving Federated Learning
by: Sun, Youbang, et al.
Published: (2024)