{"total":39,"rows":[{"id":"2204.04677","title":"FedCorr: Multi-Stage Federated Learning for Label Noise Correction","created":"2022-04-10","cited_by_count":61,"area":null},{"id":"2111.01872","title":"Towards Fairness-Aware Federated Learning","created":"2021-11-02","cited_by_count":58,"area":null},{"id":"2202.08310","title":"Towards Verifiable Federated Learning","created":"2022-02-15","cited_by_count":11,"area":null},{"id":"2211.13975","title":"FedGS: Federated Graph-based Sampling with Arbitrary Client Availability","created":"2022-11-25","cited_by_count":10,"area":null},{"id":"2305.06217","title":"Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sources","created":"2023-05-10","cited_by_count":10,"area":null},{"id":"2312.13306","title":"Towards Fair Graph Federated Learning via Incentive Mechanisms","created":"2023-12-20","cited_by_count":8,"area":null},{"id":"2502.04850","title":"Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks","created":"2025-02-07","cited_by_count":6,"area":null},{"id":"2201.02410","title":"Auction-Based Ex-Post-Payment Incentive Mechanism Design for Horizontal Federated Learning with Reputation and Contribution Measurement","created":"2022-01-07","cited_by_count":5,"area":null},{"id":"2311.14127","title":"Byzantine Robustness and Partial Participation Can Be Achieved at Once: Just Clip Gradient Differences","created":"2023-11-23","cited_by_count":5,"area":null},{"id":"2311.16459","title":"On the Effect of Defections in Federated Learning and How to Prevent Them","created":"2023-11-28","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":"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":"2305.11537","title":"Trustworthy Federated Learning: A Survey","created":"2023-05-19","cited_by_count":4,"area":null},{"id":"2405.18291","title":"FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning","created":"2024-05-28","cited_by_count":4,"area":null},{"id":"2501.12344","title":"CYCle: Choosing Your Collaborators Wisely to Enhance Collaborative Fairness in Decentralized Learning","created":"2025-01-21","cited_by_count":4,"area":null},{"id":"2206.05406","title":"Rethinking the Defense Against Free-rider Attack From the Perspective of Model Weight Evolving Frequency","created":"2022-06-11","cited_by_count":3,"area":null},{"id":"2308.07805","title":"Fairness and Privacy in Federated Learning and Their Implications in Healthcare","created":"2023-08-15","cited_by_count":3,"area":null},{"id":"2405.13879","title":"FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?","created":"2024-05-22","cited_by_count":3,"area":null},{"id":"2406.10884","title":"Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectives","created":"2024-06-16","cited_by_count":3,"area":null},{"id":"2502.05547","title":"Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning","created":"2025-02-08","cited_by_count":3,"area":null},{"id":"2505.07614","title":"Bant: Byzantine Antidote via Trial Function and Trust Scores","created":"2025-05-12","cited_by_count":3,"area":null},{"id":"2509.00799","title":"Fairness in Federated Learning: Trends, Challenges, and Opportunities","created":"2025-08-31","cited_by_count":3,"area":null},{"id":"2503.10567","title":"FedPCA: Noise-Robust Fair Federated Learning via Performance-Capacity Analysis","created":"2025-03-13","cited_by_count":2,"area":null},{"id":"2201.02783","title":"A Fair and Efficient Hybrid Federated Learning Framework based on XGBoost for Distributed Power Prediction","created":"2022-01-08","cited_by_count":1,"area":null},{"id":"2310.02651","title":"Hire When You Need to: Gradual Participant Recruitment for Auction-based Federated Learning","created":"2023-10-04","cited_by_count":1,"area":null},{"id":"2407.13881","title":"Privacy-preserving gradient-based fair federated learning","created":"2024-07-18","cited_by_count":1,"area":null},{"id":"2410.12316","title":"TPFL: A Trustworthy Personalized Federated Learning Framework via Subjective Logic","created":"2024-10-16","cited_by_count":1,"area":null},{"id":"2603.23472","title":"Byzantine-Robust and Differentially Private Federated Optimization under Weaker Assumptions","created":"2026-03-24","cited_by_count":1,"area":null},{"id":"2210.16682","title":"Robust Distributed Learning Against Both Distributional Shifts and Byzantine Attacks","created":"2022-10-29","cited_by_count":0,"area":null},{"id":"2310.13681","title":"Towards Realistic Mechanisms That Incentivize Federated Participation and Contribution","created":"2023-10-20","cited_by_count":0,"area":null},{"id":"2406.02600","title":"Data Quality in Edge Machine Learning: A State-of-the-Art Survey","created":"2024-06-01","cited_by_count":0,"area":null},{"id":"2407.16397","title":"On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and Fairness","created":"2024-07-23","cited_by_count":0,"area":null},{"id":"2410.05020","title":"FRIDA: Free-Rider Detection using Privacy Attacks","created":"2024-10-07","cited_by_count":0,"area":null},{"id":"2411.03861","title":"FedSECA: Sign Election and Coordinate-wise Aggregation of Gradients for Byzantine Tolerant Federated Learning","created":"2024-11-06","cited_by_count":0,"area":null},{"id":"2503.01324","title":"MAB-Based Channel Scheduling for Asynchronous Federated Learning in Non-Stationary Environments","created":"2025-03-03","cited_by_count":0,"area":null},{"id":"2508.12042","title":"FAIRVAR: Fair Federated Learning via Variance Regularization","created":"2025-08-16","cited_by_count":0,"area":null},{"id":"2604.04611","title":"Dynamic Free-Rider Detection in Cross-Silo Federated Learning via Simulated Attack Patterns","created":"2026-04-06","cited_by_count":0,"area":null},{"id":"2605.18892","title":"Class-wise Contribution Estimation via Logit Maximization for Federated Learning","created":"2026-05-17","cited_by_count":0,"area":null},{"id":"2608.06563","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","created":"2026-08-06","cited_by_count":0,"area":null}],"next_cursor":null}