{"total":22,"rows":[{"id":"2306.01603","title":"Decentralized Federated Learning: A Survey and Perspective","created":"2023-06-02","cited_by_count":86,"area":null},{"id":"2312.16475","title":"Federated Continual Learning via Knowledge Fusion: A Survey","created":"2023-12-27","cited_by_count":53,"area":null},{"id":"2410.05352","title":"Recent Advances of Multimodal Continual Learning: A Comprehensive Survey","created":"2024-10-07","cited_by_count":39,"area":null},{"id":"2407.06754","title":"Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges","created":"2024-07-09","cited_by_count":15,"area":null},{"id":"2305.00312","title":"Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learning","created":"2023-04-29","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":"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":"2401.00793","title":"SecFormer: Fast and Accurate Privacy-Preserving Inference for Transformer Models via SMPC","created":"2024-01-01","cited_by_count":9,"area":null},{"id":"2412.01858","title":"MQFL-FHE: Multimodal Quantum Federated Learning Framework with Fully Homomorphic Encryption","created":"2024-11-30","cited_by_count":7,"area":null},{"id":"2502.17526","title":"FedSV: Byzantine-Robust Federated Learning via Shapley Value","created":"2025-02-24","cited_by_count":6,"area":null},{"id":"2303.00897","title":"StoCFL: A Stochastically Clustered Federated Learning Framework for Non-IID Data with Dynamic Client Participation","created":"2023-03-02","cited_by_count":4,"area":null},{"id":"2411.18385","title":"Federated Learning with Uncertainty and Personalization via Efficient Second-order Optimization","created":"2024-11-27","cited_by_count":2,"area":null},{"id":"2305.09729","title":"FedHGN: A Federated Framework for Heterogeneous Graph Neural Networks","created":"2023-05-16","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":"2509.19921","title":"On the Fragility of Contribution Score Computation in Federated Learning","created":"2025-09-24","cited_by_count":1,"area":null},{"id":"2311.09680","title":"Trustworthy Large Models in Vision: A Survey","created":"2023-11-16","cited_by_count":0,"area":null},{"id":"2401.00390","title":"Horizontal Federated Computer Vision","created":"2023-12-31","cited_by_count":0,"area":null},{"id":"2405.04108","title":"A2-DIDM: Privacy-preserving Accumulator-enabled Auditing for Distributed Identity of DNN Model","created":"2024-05-07","cited_by_count":0,"area":null},{"id":"2411.16303","title":"Understanding Generalization of Federated Learning: the Trade-off between Model Stability and Optimization","created":"2024-11-25","cited_by_count":0,"area":null},{"id":"2510.22149","title":"Power to the Clients: Federated Learning in a Dictatorship Setting","created":"2025-10-25","cited_by_count":0,"area":null},{"id":"2602.08446","title":"RIFLE: Robust Distillation-based FL for Deep Model Deployment on Resource-Constrained IoT Networks","created":"2026-02-09","cited_by_count":0,"area":null},{"id":"2609.28722","title":"Upholding Robustness in Federated Learning: Trends, Emerging Strategies, and Research Opportunities","created":"2026-09-23","cited_by_count":0,"area":null}],"next_cursor":null}