{"total":35,"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":"2104.02151","title":"Distributed Learning in Wireless Networks: Recent Progress and Future Challenges","created":"2021-04-05","cited_by_count":129,"area":null},{"id":"2111.02862","title":"Parameterized Knowledge Transfer for Personalized Federated Learning","created":"2021-11-04","cited_by_count":109,"area":null},{"id":"2112.12371","title":"DENSE: Data-Free One-Shot Federated Learning","created":"2021-12-23","cited_by_count":100,"area":null},{"id":"2102.02514","title":"FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learning","created":"2021-02-04","cited_by_count":69,"area":null},{"id":"2111.11343","title":"Decentralized Federated Learning through Proxy Model Sharing","created":"2021-11-22","cited_by_count":57,"area":null},{"id":"2202.08036","title":"No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices","created":"2022-02-16","cited_by_count":37,"area":null},{"id":"2203.03106","title":"Differentially Private Federated Learning with Local Regularization and Sparsification","created":"2022-03-07","cited_by_count":36,"area":null},{"id":"2109.05659","title":"Source Inference Attacks in Federated Learning","created":"2021-09-13","cited_by_count":34,"area":null},{"id":"2303.11242","title":"Make Landscape Flatter in Differentially Private Federated Learning","created":"2023-03-20","cited_by_count":33,"area":null},{"id":"2109.03775","title":"FedZKT: Zero-Shot Knowledge Transfer towards Resource-Constrained Federated Learning with Heterogeneous On-Device Models","created":"2021-09-08","cited_by_count":31,"area":null},{"id":"2308.08643","title":"Towards Personalized Federated Learning via Heterogeneous Model Reassembly","created":"2023-08-16","cited_by_count":31,"area":null},{"id":"2401.17319","title":"Decentralized Federated Learning: A Survey on Security and Privacy","created":"2024-01-25","cited_by_count":25,"area":null},{"id":"2109.08119","title":"Personalized Federated Learning for Heterogeneous Clients with Clustered Knowledge Transfer","created":"2021-09-16","cited_by_count":23,"area":null},{"id":"2307.10655","title":"A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency","created":"2023-07-20","cited_by_count":21,"area":null},{"id":"2408.16288","title":"OpenFGL: A Comprehensive Benchmark for Federated Graph Learning","created":"2024-08-29","cited_by_count":16,"area":null},{"id":"2404.08564","title":"Federated Distillation: A Survey","created":"2024-04-02","cited_by_count":15,"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":"2401.04336","title":"Deep Efficient Private Neighbor Generation for Subgraph Federated Learning","created":"2024-01-09","cited_by_count":14,"area":null},{"id":"2206.01838","title":"Differentially Private Model Compression","created":"2022-06-03","cited_by_count":13,"area":null},{"id":"2102.09342","title":"FedMood: Federated Learning on Mobile Health Data for Mood Detection","created":"2021-02-06","cited_by_count":10,"area":null},{"id":"2211.15774","title":"Decentralized Learning with Multi-Headed Distillation","created":"2022-11-28","cited_by_count":5,"area":null},{"id":"2305.00873","title":"Towards the Flatter Landscape and Better Generalization in Federated Learning under Client-level Differential Privacy","created":"2023-05-01","cited_by_count":5,"area":null},{"id":"2305.01387","title":"Efficient Federated Learning with Enhanced Privacy via Lottery Ticket Pruning in Edge Computing","created":"2023-05-02","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":"2205.14960","title":"FedAUXfdp: Differentially Private One-Shot Federated Distillation","created":"2022-05-30","cited_by_count":3,"area":null},{"id":"2307.02140","title":"Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectives","created":"2023-07-05","cited_by_count":3,"area":null},{"id":"2204.11596","title":"A Simple Structure For Building A Robust Model","created":"2022-04-25","cited_by_count":1,"area":null},{"id":"2211.03457","title":"Closing the Gap between Client and Global Model Performance in Heterogeneous Federated Learning","created":"2022-11-07","cited_by_count":1,"area":null},{"id":"2312.04579","title":"zkDFL: An efficient and privacy-preserving decentralized federated learning with zero-knowledge proof","created":"2023-12-01","cited_by_count":1,"area":null},{"id":"2407.14710","title":"Universally Harmonizing Differential Privacy Mechanisms for Federated Learning: Boosting Accuracy and Convergence","created":"2024-07-20","cited_by_count":1,"area":null},{"id":"2508.01669","title":"Bridging Generalization Gap of Heterogeneous Federated Clients Using Generative Models","created":"2025-08-03","cited_by_count":1,"area":null},{"id":"2511.06132","title":"On the Convergence and Stability of Distributed Sub-model Training","created":"2025-11-08","cited_by_count":1,"area":null},{"id":"2301.11705","title":"FedPH: Privacy-enhanced Heterogeneous Federated Learning","created":"2023-01-27","cited_by_count":0,"area":null},{"id":"2604.07125","title":"Scalable and Private Federated Learning Using Distributed Differential Privacy and Secure Aggregation","created":"2026-04-08","cited_by_count":0,"area":null}],"next_cursor":null}