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Papers
2002.00211
Cited By

Learning to Detect Malicious Clients for Robust Federated Learning

Learning to Detect Malicious Clients for Robust Federated Learning

1 February 2020

Yang Liu

ArXiv (abs)PDF HTML

Papers citing "Learning to Detect Malicious Clients for Robust Federated Learning"

50 / 98 papers shown

BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning

BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning

Wenjing Lou

Ning Wanga

359

4

0

10 Apr 2026

Bridging the Physics-Data Gap with FNO-Guided Conditional Flow Matching: Designing Inductive Bias through Hierarchical Physical Constraints

Bridging the Physics-Data Gap with FNO-Guided Conditional Flow Matching: Designing Inductive Bias through Hierarchical Physical Constraints

181

0

0

09 Oct 2025

FairEquityFL -- A Fair and Equitable Client Selection in Federated Learning for Heterogeneous IoV Networks

FairEquityFL -- A Fair and Equitable Client Selection in Federated Learning for Heterogeneous IoV NetworksInternational Conference on Advanced Data Mining and Applications (ADMA), 2025

Noorain Mukhtiar

Kasun Eranda Wijethilake

209

3

0

24 Sep 2025

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats

218

4

0

30 Aug 2025

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks

Enrique Mármol Campos

Aurora González-Vidal

José Luis Hernández Ramos

A. Gómez-Skarmeta

114

2

0

26 Aug 2025

FedUP: Efficient Pruning-based Federated Unlearning for Model Poisoning Attacks

FedUP: Efficient Pruning-based Federated Unlearning for Model Poisoning Attacks

Nicolò Romandini

Cristian Borcea

268

1

0

19 Aug 2025

Don't Reach for the Stars: Rethinking Topology for Resilient Federated Learning

Don't Reach for the Stars: Rethinking Topology for Resilient Federated Learning

Mirko Konstantin

Anirban Mukhopadhyay

264

12

0

07 Aug 2025

ASMR: Angular Support for Malfunctioning Client Resilience in Federated Learning

ASMR: Angular Support for Malfunctioning Client Resilience in Federated LearningInternational Conference on Medical Imaging with Deep Learning (MIDL), 2025

Mirko Konstantin

Anirban Mukhopadhyay

229

0

0

04 Aug 2025

Bridging Generalization Gap of Heterogeneous Federated Clients Using Generative Models

Bridging Generalization Gap of Heterogeneous Federated Clients Using Generative Models

308

0

0

03 Aug 2025

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated LearningIEEE International Conference on Information Reuse and Integration (IRI), 2025

Likhitha Annapurna Kavuri

222

2

0

19 Jun 2025

Byzantine-Robust Federated Learning Using Generative Adversarial Networks

Byzantine-Robust Federated Learning Using Generative Adversarial Networks

André Teixeira

391

1

0

26 Mar 2025

Moss: Proxy Model-based Full-Weight Aggregation in Federated Learning with Heterogeneous Models

Moss: Proxy Model-based Full-Weight Aggregation in Federated Learning with Heterogeneous ModelsProceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies (IMWUT), 2025

536

0

0

13 Mar 2025

Runtime Backdoor Detection for Federated Learning via Representational Dissimilarity Analysis

Runtime Backdoor Detection for Federated Learning via Representational Dissimilarity AnalysisIEEE Transactions on Dependable and Secure Computing (IEEE TDSC), 2025

422

2

0

06 Mar 2025

FedSV: Byzantine-Robust Federated Learning via Shapley Value

FedSV: Byzantine-Robust Federated Learning via Shapley Value

Rachid Elazouzi

Vincent Labatut

472

11

0

24 Feb 2025

FedNIA: Noise-Induced Activation Analysis for Mitigating Data Poisoning in FL

FedNIA: Noise-Induced Activation Analysis for Mitigating Data Poisoning in FL

224

2

0

23 Feb 2025

FedEAT: A Robustness Optimization Framework for Federated LLMs

FedEAT: A Robustness Optimization Framework for Federated LLMs

486

3

0

17 Feb 2025

Decoding FL Defenses: Systemization, Pitfalls, and Remedies

Decoding FL Defenses: Systemization, Pitfalls, and Remedies

Virat Shejwalkar

Amir Houmansadr

Fatima M. Anwar

273

0

0

03 Feb 2025

FL-CLEANER: byzantine and backdoor defense by CLustering Errors of Activation maps in Non-iid fedErated leaRning

FL-CLEANER: byzantine and backdoor defense by CLustering Errors of Activation maps in Non-iid fedErated leaRning

Mehdi Ben Ghali

Reda Bellafqira

Reda Bellafqira

422

0

0

21 Jan 2025

Fine-Tuning Personalization in Federated Learning to Mitigate
Adversarial Clients

Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial ClientsNeural Information Processing Systems (NeurIPS), 2024

Youssef Allouah

Abdellah El Mrini

221

8

0

30 Sep 2024

Leveraging Pre-trained Models for Robust Federated Learning for Kidney
Stone Type Recognition

Leveraging Pre-trained Models for Robust Federated Learning for Kidney Stone Type RecognitionMexican International Conference on Artificial Intelligence (MICAI), 2024

Iván Reyes-Amezcua

Michael Rojas-Ruiz

Gilberto Ochoa-Ruiz

Andres Mendez-Vazquez

171

2

0

30 Sep 2024

Data Distribution Shifts in (Industrial) Federated Learning as a Privacy
Issue

Data Distribution Shifts in (Industrial) Federated Learning as a Privacy Issue

Alessio Montuoro

189

0

0

20 Sep 2024

Data Poisoning and Leakage Analysis in Federated Learning

Data Poisoning and Leakage Analysis in Federated Learning

Tiansheng Huang

Ling Liu

322

2

0

19 Sep 2024

DART: A Solution for Decentralized Federated Learning Model Robustness
Analysis

DART: A Solution for Decentralized Federated Learning Model Robustness Analysis

Alberto Huertas Celdrán

Jan von der Assen

Enrique Tomás Martínez Beltrán

Burkhard Stiller

262

19

0

11 Jul 2024

Non-Cooperative Backdoor Attacks in Federated Learning: A New Threat
Landscape

Non-Cooperative Backdoor Attacks in Federated Learning: A New Threat Landscape

Tuan Nguyen

Dung Thuy Nguyen

234

3

0

05 Jul 2024

A Whole-Process Certifiably Robust Aggregation Method Against Backdoor
Attacks in Federated Learning

A Whole-Process Certifiably Robust Aggregation Method Against Backdoor Attacks in Federated Learning

Yezheng Liu

209

1

0

30 Jun 2024

Communication-Efficient Byzantine-Resilient Federated Zero-Order
Optimization

Communication-Efficient Byzantine-Resilient Federated Zero-Order Optimization

Afonso de Sá Delgado Neto

Maximilian Egger

196

3

0

20 Jun 2024

Certified Robustness to Data Poisoning in Gradient-Based Training

Certified Robustness to Data Poisoning in Gradient-Based Training

Mark N. Müller

Maximilian Baader

321

19

0

09 Jun 2024

Marking the Pace: A Blockchain-Enhanced Privacy-Traceable Strategy for
Federated Recommender Systems

Marking the Pace: A Blockchain-Enhanced Privacy-Traceable Strategy for Federated Recommender SystemsIEEE Internet of Things Journal (IEEE IoT J.), 2024

358

8

0

07 Jun 2024

Federated Learning in Healthcare: Model Misconducts, Security,
Challenges, Applications, and Future Research Directions -- A Systematic
Review

Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Review

Maruf Md. Sajjad Hossain

Md Mahfuz Ahmed

Md. Khairul Islam

Shivakumar Raman

255

30

0

22 May 2024

Model Poisoning Attacks to Federated Learning via Multi-Round Consistency

Model Poisoning Attacks to Federated Learning via Multi-Round Consistency

Neil Zhenqiang Gong

394

35

0

24 Apr 2024

Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data

Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data

Dayananda Herurkar

Sebastián M. Palacio

287

7

0

23 Apr 2024

FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive
Models

FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive Models

Woorim Han

248

5

0

05 Mar 2024

Decentralized Federated Learning: A Survey on Security and Privacy

Decentralized Federated Learning: A Survey on Security and PrivacyIEEE Transactions on Big Data (IEEE Trans. Big Data), 2024

366

116

0

25 Jan 2024

Towards Efficient and Certified Recovery from Poisoning Attacks in
Federated Learning

Towards Efficient and Certified Recovery from Poisoning Attacks in Federated LearningIEEE Transactions on Information Forensics and Security (IEEE TIFS), 2024

Kwok-Yan Lam

426

20

0

16 Jan 2024

Data and Model Poisoning Backdoor Attacks on Wireless Federated
Learning, and the Defense Mechanisms: A Comprehensive Survey

Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive SurveyIEEE Communications Surveys and Tutorials (COMST), 2023

343

97

0

14 Dec 2023

Defenses in Adversarial Machine Learning: A Survey

Defenses in Adversarial Machine Learning: A Survey

Hongrui Chen

367

31

0

13 Dec 2023

A Survey on Federated Unlearning: Challenges, Methods, and Future
Directions

A Survey on Federated Unlearning: Challenges, Methods, and Future DirectionsACM Computing Surveys (ACM Comput. Surv.), 2023

Kwok-Yan Lam

Xiaoning Liu

497

119

0

31 Oct 2023

FLrce: Resource-Efficient Federated Learning with Early-Stopping
Strategy

FLrce: Resource-Efficient Federated Learning with Early-Stopping Strategy

Senior Member Ieee Hai Dong

Senior Member Ieee Tao Gu

330

22

0

15 Oct 2023

Kick Bad Guys Out! Conditionally Activated Anomaly Detection in Federated Learning with Zero-Knowledge Proof Verification

Kick Bad Guys Out! Conditionally Activated Anomaly Detection in Federated Learning with Zero-Knowledge Proof Verification

Baturalp Buyukates

Salman Avestimehr

536

1

0

06 Oct 2023

Resisting Backdoor Attacks in Federated Learning via Bidirectional
Elections and Individual Perspective

Resisting Backdoor Attacks in Federated Learning via Bidirectional Elections and Individual PerspectiveAAAI Conference on Artificial Intelligence (AAAI), 2023

Zhen Qin

232

20

0

28 Sep 2023

SPIRT: A Fault-Tolerant and Reliable Peer-to-Peer Serverless ML Training
Architecture

SPIRT: A Fault-Tolerant and Reliable Peer-to-Peer Serverless ML Training ArchitectureInternational Conference on Software Quality, Reliability and Security (QRS), 2023

Fábio Petrillo

237

6

0

25 Sep 2023

SPFL: A Self-purified Federated Learning Method Against Poisoning
Attacks

SPFL: A Self-purified Federated Learning Method Against Poisoning AttacksIEEE Transactions on Information Forensics and Security (IEEE TIFS), 2023

Chip-Hong Chang

Jing Ye

312

12

0

19 Sep 2023

When Federated Learning meets Watermarking: A Comprehensive Overview of
Techniques for Intellectual Property Protection

When Federated Learning meets Watermarking: A Comprehensive Overview of Techniques for Intellectual Property ProtectionMachine Learning and Knowledge Extraction (MLKE), 2023

Mohammed Lansari

Reda Bellafqira

Reda Bellafqira

171

32

0

07 Aug 2023

Heterogeneous Federated Learning: State-of-the-art and Research
Challenges

Heterogeneous Federated Learning: State-of-the-art and Research ChallengesACM Computing Surveys (ACM Comput. Surv.), 2023

Bo Du

520

553

0

20 Jul 2023

FedVal: Different good or different bad in federated learning

FedVal: Different good or different bad in federated learning

Nicholas D. Lane

285

7

0

06 Jun 2023

Covert Communication Based on the Poisoning Attack in Federated Learning

Covert Communication Based on the Poisoning Attack in Federated Learning

224

1

0

02 Jun 2023

Trustworthy Federated Learning: A Survey

Trustworthy Federated Learning: A Survey

332

19

0

19 May 2023

FedGT: Identification of Malicious Clients in Federated Learning with
Secure Aggregation

FedGT: Identification of Malicious Clients in Federated Learning with Secure AggregationIEEE Transactions on Information Forensics and Security (IEEE TIFS), 2023

Antonia Wachter-Zeh

Alexandre Graell i Amat

390

35

0

09 May 2023

Multi-metrics adaptively identifies backdoors in Federated learning

Multi-metrics adaptively identifies backdoors in Federated learningIEEE International Conference on Computer Vision (ICCV), 2023

358

54

0

12 Mar 2023

Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges
and Future Research Directions

Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research DirectionsEngineering applications of artificial intelligence (Eng. Appl. Artif. Intell.), 2023

Thuy-Dung Nguyen

208

103

0

03 Mar 2023

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