{"total":20,"rows":[{"id":"2106.09580","title":"Optimality and Stability in Federated Learning: A Game-theoretic Approach","created":"2021-06-17","cited_by_count":40,"area":null},{"id":"2209.10526","title":"Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles Between Client Data Subspaces","created":"2022-09-21","cited_by_count":36,"area":null},{"id":"2210.07615","title":"FedFM: Anchor-based Feature Matching for Data Heterogeneity in Federated Learning","created":"2022-10-14","cited_by_count":27,"area":null},{"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":"2208.10993","title":"Application of federated learning techniques for arrhythmia classification using 12-lead ECG signals","created":"2022-08-23","cited_by_count":9,"area":null},{"id":"2210.02614","title":"Federated Learning with Server Learning: Enhancing Performance for Non-IID Data","created":"2022-10-06","cited_by_count":6,"area":null},{"id":"2312.04504","title":"Coordination-free Decentralised Federated Learning on Complex Networks: Overcoming Heterogeneity","created":"2023-12-07","cited_by_count":6,"area":null},{"id":"2311.16535","title":"Contrastive encoder pre-training-based clustered federated learning for heterogeneous data","created":"2023-11-28","cited_by_count":5,"area":null},{"id":"2503.17070","title":"A Thorough Assessment of the Non-IID Data Impact in Federated Learning","created":"2025-03-21","cited_by_count":5,"area":null},{"id":"2105.00602","title":"OCTOPUS: Overcoming Performance andPrivatization Bottlenecks in Distributed Learning","created":"2021-05-03","cited_by_count":2,"area":null},{"id":"2308.13714","title":"Uncovering Promises and Challenges of Federated Learning to Detect Cardiovascular Diseases: A Scoping Literature Review","created":"2023-08-26","cited_by_count":2,"area":null},{"id":"2501.17512","title":"A survey on Clustered Federated Learning: Taxonomy, Analysis and Applications","created":"2025-01-29","cited_by_count":2,"area":null},{"id":"2404.15598","title":"Federated Learning with Only Positive Labels by Exploring Label Correlations","created":"2024-04-24","cited_by_count":1,"area":null},{"id":"2302.02531","title":"Cross-Fusion Rule for Personalized Federated Learning","created":"2023-02-06","cited_by_count":0,"area":null},{"id":"2303.02445","title":"Federated Semi-Supervised Learning with Annotation Heterogeneity","created":"2023-03-04","cited_by_count":0,"area":null},{"id":"2308.13158","title":"DAG-ACFL: Asynchronous Clustered Federated Learning based on DAG-DLT","created":"2023-08-25","cited_by_count":0,"area":null},{"id":"2403.19792","title":"MAPL: Model Agnostic Peer-to-peer Learning","created":"2024-03-28","cited_by_count":0,"area":null},{"id":"2407.12287","title":"CDFL: Efficient Federated Human Activity Recognition using Contrastive Learning and Deep Clustering","created":"2024-07-17","cited_by_count":0,"area":null},{"id":"2508.01515","title":"SimDeep: Federated 3D Indoor Localization via Similarity-Aware Aggregation","created":"2025-08-02","cited_by_count":0,"area":null},{"id":"2603.16370","title":"FederatedFactory: Generative One-Shot Learning for Extremely Non-IID Distributed Scenarios","created":"2026-03-17","cited_by_count":0,"area":null}],"next_cursor":null}