Cover Image

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

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Xhemrishi, Marvin, Amat, Alexandre Graell i, Pejó, Balázs
Format:	Preprint
Published:	2025
Subjects:	Cryptography and Security Distributed, Parallel, and Cluster Computing Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Lightweight Federated Learning Approach for Privacy-Preserving Botnet Detection in IoT
by: Mahmoud, Taha M., et al.
Published: (2025)

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

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

Privacy Preserving Machine Learning Model Personalization through Federated Personalized Learning
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)

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

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

A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated Learning
by: Kang, Yan, et al.
Published: (2022)

Federated Cyber Defense: Privacy-Preserving Ransomware Detection Across Distributed Systems
by: Jimenez-Gutierrez, Daniel M., et al.
Published: (2025)

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

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

On the Tradeoff between Privacy Preservation and Byzantine-Robustness in Decentralized Learning
by: Ye, Haoxiang, et al.
Published: (2023)

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

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

Share Secrets for Privacy: Confidential Forecasting with Vertical Federated Learning
by: Shankar, Aditya, et al.
Published: (2024)

Differentially Private Federated Learning With Time-Adaptive Privacy Spending
by: Kiani, Shahrzad, et al.
Published: (2025)

Detecting Backdoor Attacks in Federated Learning via Direction Alignment Inspection
by: Xu, Jiahao, et al.
Published: (2025)

Hear No Evil: Detecting Gradient Leakage by Malicious Servers in Federated Learning
by: Wang, Fei, et al.
Published: (2025)

Adaptive Differential Privacy in Federated Learning: A Priority-Based Approach
by: Talaei, Mahtab, et al.
Published: (2024)

Enhancing Federated Learning with Adaptive Differential Privacy and Priority-Based Aggregation
by: Talaei, Mahtab, et al.
Published: (2024)

The Effect of Quantization in Federated Learning: A Rényi Differential Privacy Perspective
by: Kang, Tianqu, et al.
Published: (2024)

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)

Federated Learning for Pediatric Pneumonia Detection: Enabling Collaborative Diagnosis Without Sharing Patient Data
by: Jimenez-Gutierrez, Daniel M., et al.
Published: (2025)

zkFL-Health: Blockchain-Enabled Zero-Knowledge Federated Learning for Medical AI Privacy
by: Sharma, Savvy, 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: (2023)

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

Advances in Privacy Preserving Federated Learning to Realize a Truly Learning Healthcare System
by: Madduri, Ravi, et al.
Published: (2024)

Quantum-Inspired Privacy-Preserving Federated Learning Framework for Secure Dementia Classification
by: Tanbhir, Gazi, et al.
Published: (2025)

Towards Fair, Robust and Efficient Client Contribution Evaluation in Federated Learning
by: Zhang, Meiying, et al.
Published: (2024)

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

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

Not All Edges are Equally Robust: Evaluating the Robustness of Ranking-Based Federated Learning
by: Gong, Zirui, et al.
Published: (2025)

Robust Zero Trust Architecture: Joint Blockchain based Federated learning and Anomaly Detection based Framework
by: Pokhrel, Shiva Raj, et al.
Published: (2024)

TimberStrike: Dataset Reconstruction Attack Revealing Privacy Leakage in Federated Tree-Based Systems
by: Di Gennaro, Marco, et al.
Published: (2025)

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

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

Towards Trustworthy Federated Learning
by: Basharat, Alina, et al.
Published: (2025)

Decentralized Collaborative Learning Framework with External Privacy Leakage Analysis
by: Idé, Tsuyoshi, 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)