{"total":15,"rows":[{"id":"2411.12377","title":"Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions","created":"2024-11-19","cited_by_count":13,"area":null},{"id":"2404.13244","title":"Intelligent Agents for Auction-based Federated Learning: A Survey","created":"2024-04-20","cited_by_count":4,"area":null},{"id":"2311.12548","title":"Multi-Session Budget Optimization for Forward Auction-based Federated Learning","created":"2023-11-21","cited_by_count":3,"area":null},{"id":"2401.02740","title":"Fairness-Aware Job Scheduling for Multi-Job Federated Learning","created":"2024-01-05","cited_by_count":1,"area":null},{"id":"2405.13584","title":"Emulating Full Participation: An Effective and Fair Client Selection Strategy for Federated Learning","created":"2024-05-22","cited_by_count":1,"area":null},{"id":"2504.12577","title":"Local Data Quantity-Aware Weighted Averaging for Federated Learning with Dishonest Clients","created":"2025-04-17","cited_by_count":0,"area":null},{"id":"2506.15923","title":"PNCS:Power-Norm Cosine Similarity for Diverse Client Selection in Federated Learning","created":"2025-06-18","cited_by_count":0,"area":null},{"id":"2508.06692","title":"HeteRo-Select: Informativeness as the Participation Driver in Heterogeneous Federated Learning","created":"2025-08-08","cited_by_count":0,"area":null},{"id":"2509.20193","title":"FairEquityFL -- A Fair and Equitable Client Selection in Federated Learning for Heterogeneous IoV Networks","created":"2025-09-24","cited_by_count":0,"area":null},{"id":"2511.01737","title":"Edge AI in Highly Volatile Environments: Is Fairness Worth the Accuracy Trade-off?","created":"2025-11-03","cited_by_count":0,"area":null},{"id":"2511.15454","title":"FairEnergy: Contribution-Based Fairness meets Energy Efficiency in Federated Learning","created":"2025-11-19","cited_by_count":0,"area":null},{"id":"2602.00718","title":"Federated Learning at the Forefront of Fairness: A Multifaceted Perspective","created":"2026-01-31","cited_by_count":0,"area":null},{"id":"2602.13651","title":"Cumulative Utility Parity for Fair Federated Learning under Intermittent Client Participation","created":"2026-02-14","cited_by_count":0,"area":null},{"id":"2606.18384","title":"SCOPE-FL: A Strategy-proof Chain-based Optimal pareto efficient Federated Learning System","created":"2026-06-16","cited_by_count":0,"area":null},{"id":"2608.02250","title":"Assessing the Impacts of Imperfect Datasets on Client Selections in Federated Learning","created":"2026-08-03","cited_by_count":0,"area":null}],"next_cursor":null}