{"total":21,"rows":[{"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":"2305.02231","title":"Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation","created":"2023-05-02","cited_by_count":61,"area":null},{"id":"2202.06924","title":"Do Gradient Inversion Attacks Make Federated Learning Unsafe?","created":"2022-02-14","cited_by_count":44,"area":null},{"id":"2204.05562","title":"FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning","created":"2022-04-12","cited_by_count":43,"area":null},{"id":"2208.03392","title":"Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions","created":"2022-08-05","cited_by_count":35,"area":null},{"id":"2211.07893","title":"Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges","created":"2022-11-15","cited_by_count":34,"area":null},{"id":"2207.08581","title":"Study of the performance and scalability of federated learning for medical imaging with intermittent clients","created":"2022-07-18","cited_by_count":9,"area":null},{"id":"2111.04105","title":"DQRE-SCnet: A novel hybrid approach for selecting users in Federated Learning with Deep-Q-Reinforcement Learning based on Spectral Clustering","created":"2021-11-07","cited_by_count":7,"area":null},{"id":"2404.02510","title":"An Interpretable Client Decision Tree Aggregation process for Federated Learning","created":"2024-04-03","cited_by_count":5,"area":null},{"id":"2211.06579","title":"Explainable Artificial Intelligence: Precepts, Methods, and Opportunities for Research in Construction","created":"2022-11-12","cited_by_count":4,"area":null},{"id":"2105.10545","title":"HyFed: A Hybrid Federated Framework for Privacy-preserving Machine Learning","created":"2021-05-21","cited_by_count":3,"area":null},{"id":"2508.18803","title":"A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks","created":"2025-08-26","cited_by_count":3,"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":"2307.14283","title":"General Purpose Artificial Intelligence Systems (GPAIS): Properties, Definition, Taxonomy, Societal Implications and Responsible Governance","created":"2023-07-26","cited_by_count":1,"area":null},{"id":"2402.02672","title":"Estimation of conditional average treatment effects on distributed confidential data","created":"2024-02-05","cited_by_count":1,"area":null},{"id":"2506.17805","title":"AdRo-FL: Informed and Secure Client Selection for Federated Learning in the Presence of Adversarial Aggregator","created":"2025-06-21","cited_by_count":1,"area":null},{"id":"2202.13626","title":"Improving Response Time of Home IoT Services in Federated Learning","created":"2022-02-28","cited_by_count":0,"area":null},{"id":"2212.11729","title":"Federated Learning -- Methods, Applications and beyond","created":"2022-12-22","cited_by_count":0,"area":null},{"id":"2307.14381","title":"EdgeConvEns: Convolutional Ensemble Learning for Edge Intelligence","created":"2023-07-25","cited_by_count":0,"area":null},{"id":"2311.10832","title":"Exploring Machine Learning Models for Federated Learning: A Review of Approaches, Performance, and Limitations","created":"2023-11-17","cited_by_count":0,"area":null},{"id":"2408.15621","title":"Convergent Differential Privacy Analysis for General Federated Learning","created":"2024-08-28","cited_by_count":0,"area":null}],"next_cursor":null}