{"total":145,"rows":[{"id":"2307.10616","title":"Heterogeneous Federated Learning: State-of-the-art and Research Challenges","created":"2023-07-20","cited_by_count":161,"area":null},{"id":"2311.06750","title":"Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark","created":"2023-11-12","cited_by_count":99,"area":null},{"id":"2201.08135","title":"Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges","created":"2022-01-20","cited_by_count":84,"area":null},{"id":"2108.11887","title":"Federated Reinforcement Learning: Techniques, Applications, and Open Challenges","created":"2021-08-26","cited_by_count":81,"area":null},{"id":"2104.14362","title":"From Distributed Machine Learning to Federated Learning: A Survey","created":"2021-04-29","cited_by_count":66,"area":null},{"id":"2112.14468","title":"Challenges and Approaches for Mitigating Byzantine Attacks in Federated Learning","created":"2021-12-29","cited_by_count":47,"area":null},{"id":"2106.06089","title":"Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix","created":"2021-06-10","cited_by_count":43,"area":null},{"id":"2309.05610","title":"Privacy Side Channels in Machine Learning Systems","created":"2023-09-11","cited_by_count":40,"area":null},{"id":"2110.13864","title":"FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective","created":"2021-10-26","cited_by_count":36,"area":null},{"id":"2303.02213","title":"Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directions","created":"2023-03-03","cited_by_count":33,"area":null},{"id":"2111.11066","title":"FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks","created":"2021-11-22","cited_by_count":32,"area":null},{"id":"2201.02873","title":"LoMar: A Local Defense Against Poisoning Attack on Federated Learning","created":"2022-01-08","cited_by_count":31,"area":null},{"id":"2205.11765","title":"Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guarantees","created":"2022-05-24","cited_by_count":30,"area":null},{"id":"2207.06343","title":"TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels","created":"2022-07-13","cited_by_count":26,"area":null},{"id":"2107.03311","title":"RoFL: Robustness of Secure Federated Learning","created":"2021-07-07","cited_by_count":25,"area":null},{"id":"2307.00974","title":"Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions","created":"2023-07-03","cited_by_count":21,"area":null},{"id":"2101.03705","title":"FedAR: Activity and Resource-Aware Federated Learning Model for Distributed Mobile Robots","created":"2021-01-11","cited_by_count":18,"area":null},{"id":"2209.07124","title":"The Cost of Training Machine Learning Models over Distributed Data Sources","created":"2022-09-15","cited_by_count":18,"area":null},{"id":"2305.05506","title":"FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation","created":"2023-05-09","cited_by_count":16,"area":null},{"id":"2108.03980","title":"Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness","created":"2021-07-30","cited_by_count":15,"area":null},{"id":"2109.12062","title":"SGDE: Secure Generative Data Exchange for Cross-Silo Federated Learning","created":"2021-09-24","cited_by_count":15,"area":null},{"id":"2202.03195","title":"More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks","created":"2022-02-07","cited_by_count":15,"area":null},{"id":"2205.07855","title":"Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review","created":"2022-05-07","cited_by_count":15,"area":null},{"id":"2501.17381","title":"Do We Really Need to Design New Byzantine-robust Aggregation Rules?","created":"2025-01-29","cited_by_count":15,"area":null},{"id":"2101.03300","title":"Robust Blockchained Federated Learning with Model Validation and Proof-of-Stake Inspired Consensus","created":"2021-01-09","cited_by_count":14,"area":null},{"id":"2312.08890","title":"Defenses in Adversarial Machine Learning: A Survey","created":"2023-12-13","cited_by_count":14,"area":null},{"id":"2101.10904","title":"Untargeted Poisoning Attack Detection in Federated Learning via Behavior Attestation","created":"2021-01-24","cited_by_count":13,"area":null},{"id":"2211.14952","title":"Federated Learning Attacks and Defenses: A Survey","created":"2022-11-27","cited_by_count":12,"area":null},{"id":"2411.01583","title":"Trustworthy Federated Learning: Privacy, Security, and Beyond","created":"2024-11-03","cited_by_count":11,"area":null},{"id":"2101.03627","title":"Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks","created":"2021-01-10","cited_by_count":10,"area":null},{"id":"2304.08847","title":"BadVFL: Backdoor Attacks in Vertical Federated Learning","created":"2023-04-18","cited_by_count":10,"area":null},{"id":"2404.14389","title":"Poisoning Attacks on Federated Learning-based Wireless Traffic Prediction","created":"2024-04-22","cited_by_count":10,"area":null},{"id":"2106.04384","title":"FL-Market: Trading Private Models in Federated Learning","created":"2021-06-08","cited_by_count":9,"area":null},{"id":"2107.08402","title":"RobustFed: A Truth Inference Approach for Robust Federated Learning","created":"2021-07-18","cited_by_count":9,"area":null},{"id":"2201.02775","title":"ADI: Adversarial Dominating Inputs in Vertical Federated Learning Systems","created":"2022-01-08","cited_by_count":9,"area":null},{"id":"2204.14017","title":"Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient Ensembling","created":"2022-04-29","cited_by_count":9,"area":null},{"id":"2303.03908","title":"Client-specific Property Inference against Secure Aggregation in Federated Learning","created":"2023-03-07","cited_by_count":9,"area":null},{"id":"2306.04984","title":"G$^2$uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering","created":"2023-06-08","cited_by_count":9,"area":null},{"id":"2311.16065","title":"A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspective","created":"2023-11-27","cited_by_count":9,"area":null},{"id":"2210.14830","title":"Personalized Federated Learning via Heterogeneous Modular Networks","created":"2022-10-26","cited_by_count":8,"area":null},{"id":"2101.00159","title":"Fidel: Reconstructing Private Training Samples from Weight Updates in Federated Learning","created":"2021-01-01","cited_by_count":7,"area":null},{"id":"2305.08070","title":"A Survey of Federated Evaluation in Federated Learning","created":"2023-05-14","cited_by_count":7,"area":null},{"id":"2104.10561","title":"Turning Federated Learning Systems Into Covert Channels","created":"2021-04-21","cited_by_count":6,"area":null},{"id":"2309.16456","title":"Resisting Backdoor Attacks in Federated Learning via Bidirectional Elections and Individual Perspective","created":"2023-09-28","cited_by_count":6,"area":null},{"id":"2406.06207","title":"Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learning","created":"2024-06-10","cited_by_count":6,"area":null},{"id":"2505.04873","title":"Federated Learning for Cyber Physical Systems: A Comprehensive Survey","created":"2025-05-08","cited_by_count":6,"area":null},{"id":"2102.02402","title":"SAFELearning: Enable Backdoor Detectability In Federated Learning With Secure Aggregation","created":"2021-02-04","cited_by_count":5,"area":null},{"id":"2205.01184","title":"Performance Weighting for Robust Federated Learning Against Corrupted Sources","created":"2022-05-02","cited_by_count":5,"area":null},{"id":"2205.11736","title":"Towards a Defense Against Federated Backdoor Attacks Under Continuous Training","created":"2022-05-24","cited_by_count":5,"area":null},{"id":"2212.00322","title":"Hijack Vertical Federated Learning Models As One Party","created":"2022-12-01","cited_by_count":5,"area":null},{"id":"2302.14031","title":"Proof-of-Contribution-Based Design for Collaborative Machine Learning on Blockchain","created":"2023-02-27","cited_by_count":5,"area":null},{"id":"2311.15310","title":"Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs","created":"2023-11-26","cited_by_count":5,"area":null},{"id":"2405.20975","title":"ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning","created":"2024-05-31","cited_by_count":5,"area":null},{"id":"2410.11205","title":"Adversarially Guided Stateful Defense Against Backdoor Attacks in Federated Deep Learning","created":"2024-10-15","cited_by_count":5,"area":null},{"id":"2107.12490","title":"LEGATO: A LayerwisE Gradient AggregaTiOn Algorithm for Mitigating Byzantine Attacks in Federated Learning","created":"2021-07-26","cited_by_count":4,"area":null},{"id":"2109.00202","title":"Federated Learning: Issues in Medical Application","created":"2021-09-01","cited_by_count":4,"area":null},{"id":"2110.11006","title":"Bristle: Decentralized Federated Learning in Byzantine, Non-i.i.d. Environments","created":"2021-10-21","cited_by_count":4,"area":null},{"id":"2202.11196","title":"Backdoor Defense in Federated Learning Using Differential Testing and Outlier Detection","created":"2022-02-21","cited_by_count":4,"area":null},{"id":"2204.12443","title":"A review of Federated Learning in Intrusion Detection Systems for IoT","created":"2022-04-26","cited_by_count":4,"area":null},{"id":"2207.02337","title":"Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms","created":"2022-07-05","cited_by_count":4,"area":null},{"id":"2207.05978","title":"Enhanced Security and Privacy via Fragmented Federated Learning","created":"2022-07-13","cited_by_count":4,"area":null},{"id":"2207.08486","title":"Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications","created":"2022-07-18","cited_by_count":4,"area":null},{"id":"2302.09913","title":"ByzSecAgg: A Byzantine-Resistant Secure Aggregation Scheme for Federated Learning Based on Coded Computing and Vector Commitment","created":"2023-02-20","cited_by_count":4,"area":null},{"id":"2305.11537","title":"Trustworthy Federated Learning: A Survey","created":"2023-05-19","cited_by_count":4,"area":null},{"id":"2406.14362","title":"Communication-Efficient Byzantine-Resilient Federated Zero-Order Optimization","created":"2024-06-20","cited_by_count":4,"area":null},{"id":"2504.17703","title":"Federated Learning: A Survey on Privacy-Preserving Collaborative Intelligence","created":"2025-04-24","cited_by_count":4,"area":null},{"id":"2207.12327","title":"Technical Report: Assisting Backdoor Federated Learning with Whole Population Knowledge Alignment","created":"2022-07-25","cited_by_count":3,"area":null},{"id":"2211.05554","title":"Robust Federated Learning against both Data Heterogeneity and Poisoning Attack via Aggregation Optimization","created":"2022-11-10","cited_by_count":3,"area":null},{"id":"2310.10665","title":"Privacy Preservation in Artificial Intelligence and Extended Reality (AI-XR) Metaverses: A Survey","created":"2023-09-19","cited_by_count":3,"area":null},{"id":"2402.10082","title":"FedRDF: A Robust and Dynamic Aggregation Function against Poisoning Attacks in Federated Learning","created":"2024-02-15","cited_by_count":3,"area":null},{"id":"2407.09658","title":"BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning","created":"2024-07-12","cited_by_count":3,"area":null},{"id":"2501.09320","title":"Cooperative Decentralized Backdoor Attacks on Vertical Federated Learning","created":"2025-01-16","cited_by_count":3,"area":null},{"id":"2512.17254","title":"Practical Framework for Privacy-Preserving and Byzantine-robust Federated Learning","created":"2025-12-19","cited_by_count":3,"area":null},{"id":"2105.08671","title":"DID-eFed: Facilitating Federated Learning as a Service with Decentralized Identities","created":"2021-05-18","cited_by_count":2,"area":null},{"id":"2110.11619","title":"DistFL: Distribution-aware Federated Learning for Mobile Scenarios","created":"2021-10-22","cited_by_count":2,"area":null},{"id":"2208.00848","title":"DeFL: Decentralized Weight Aggregation for Cross-silo Federated Learning","created":"2022-08-01","cited_by_count":2,"area":null},{"id":"2210.02873","title":"Blockchain-based Monitoring for Poison Attack Detection in Decentralized Federated Learning","created":"2022-09-30","cited_by_count":2,"area":null},{"id":"2403.04803","title":"Enhancing Security in Federated Learning through Adaptive Consensus-Based Model Update Validation","created":"2024-03-05","cited_by_count":2,"area":null},{"id":"2407.15389","title":"Poisoning with A Pill: Circumventing Detection in Federated Learning","created":"2024-07-22","cited_by_count":2,"area":null},{"id":"2407.18096","title":"Privacy Threats and Countermeasures in Federated Learning for Internet of Things: A Systematic Review","created":"2024-07-25","cited_by_count":2,"area":null},{"id":"2502.16545","title":"Multi-Target Federated Backdoor Attack Based on Feature Aggregation","created":"2025-02-23","cited_by_count":2,"area":null},{"id":"2506.09674","title":"Wavelet Scattering Transform and Fourier Representation for Offline Detection of Malicious Clients in Federated Learning","created":"2025-06-11","cited_by_count":2,"area":null},{"id":"2508.13730","title":"On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future Directions","created":"2025-08-19","cited_by_count":2,"area":null},{"id":"2102.10799","title":"Clustering Algorithm to Detect Adversaries in Federated Learning","created":"2021-02-22","cited_by_count":1,"area":null},{"id":"2111.04550","title":"ARFED: Attack-Resistant Federated averaging based on outlier elimination","created":"2021-11-08","cited_by_count":1,"area":null},{"id":"2201.12701","title":"DearFSAC: An Approach to Optimizing Unreliable Federated Learning via Deep Reinforcement Learning","created":"2022-01-30","cited_by_count":1,"area":null},{"id":"2202.04311","title":"Identifying Backdoor Attacks in Federated Learning via Anomaly Detection","created":"2022-02-09","cited_by_count":1,"area":null},{"id":"2204.13697","title":"Federated Learning: Balancing the Thin Line Between Data Intelligence and Privacy","created":"2022-04-22","cited_by_count":1,"area":null},{"id":"2310.00616","title":"Towards Understanding Adversarial Transferability in Federated Learning","created":"2023-10-01","cited_by_count":1,"area":null},{"id":"2311.03369","title":"Can We Trust the Similarity Measurement in Federated Learning?","created":"2023-10-20","cited_by_count":1,"area":null},{"id":"2405.06206","title":"Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning","created":"2024-05-10","cited_by_count":1,"area":null},{"id":"2408.06042","title":"Understanding Byzantine Robustness in Federated Learning with A Black-box Server","created":"2024-08-12","cited_by_count":1,"area":null},{"id":"2409.13451","title":"Noise-Robust and Resource-Efficient ADMM-based Federated Learning","created":"2024-09-20","cited_by_count":1,"area":null},{"id":"2409.18907","title":"In-depth Analysis of Privacy Threats in Federated Learning for Medical Data","created":"2024-09-27","cited_by_count":1,"area":null},{"id":"2410.22680","title":"Byzantine-Robust Federated Learning: An Overview With Focus on Developing Sybil-based Attacks to Backdoor Augmented Secure Aggregation Protocols","created":"2024-10-30","cited_by_count":1,"area":null},{"id":"2506.23583","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","created":"2025-06-30","cited_by_count":1,"area":null},{"id":"2507.04903","title":"BackFed: An Efficient & Standardized Benchmark Suite for Backdoor Attacks in Federated Learning","created":"2025-07-07","cited_by_count":1,"area":null},{"id":"2508.18737","title":"FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks","created":"2025-08-26","cited_by_count":1,"area":null},{"id":"2508.20333","title":"Poison Once, Refuse Forever: Weaponizing Alignment for Injecting Bias in LLMs","created":"2025-08-28","cited_by_count":1,"area":null},{"id":"2509.00634","title":"Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats","created":"2025-08-30","cited_by_count":1,"area":null}],"next_cursor":"100"}