{"total":23,"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":"2305.16854","title":"Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning","created":"2023-05-26","cited_by_count":9,"area":null},{"id":"2503.20117","title":"Exact and Linear Convergence for Federated Learning under Arbitrary Client Participation is Attainable","created":"2025-03-25","cited_by_count":6,"area":null},{"id":"2301.12407","title":"Entropy-driven Fair and Effective Federated Learning","created":"2023-01-29","cited_by_count":4,"area":null},{"id":"2501.15259","title":"Scalable Decentralized Learning with Teleportation","created":"2025-01-25","cited_by_count":3,"area":null},{"id":"2212.13679","title":"CC-FedAvg: Computationally Customized Federated Averaging","created":"2022-12-28","cited_by_count":2,"area":null},{"id":"2310.02698","title":"Enhanced Federated Optimization: Adaptive Unbiased Client Sampling with Reduced Variance","created":"2023-10-04","cited_by_count":2,"area":null},{"id":"2407.04460","title":"Smart Sampling: Helping from Friendly Neighbors for Decentralized Federated Learning","created":"2024-07-05","cited_by_count":2,"area":null},{"id":"2408.08642","title":"The Power of Bias: Optimizing Client Selection in Federated Learning with Heterogeneous Differential Privacy","created":"2024-08-16","cited_by_count":2,"area":null},{"id":"2410.20141","title":"FedMABA: Towards Fair Federated Learning through Multi-Armed Bandits Allocation","created":"2024-10-26","cited_by_count":1,"area":null},{"id":"2505.09704","title":"Energy-Efficient Federated Learning for AIoT using Clustering Methods","created":"2025-05-14","cited_by_count":1,"area":null},{"id":"2505.11304","title":"Heterogeneity-Aware Client Sampling for Optimal and Efficient Federated Learning","created":"2025-05-16","cited_by_count":1,"area":null},{"id":"2505.13655","title":"Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy","created":"2025-05-19","cited_by_count":1,"area":null},{"id":"2310.20193","title":"FedRec+: Enhancing Privacy and Addressing Heterogeneity in Federated Recommendation Systems","created":"2023-10-31","cited_by_count":0,"area":null},{"id":"2405.16233","title":"Client2Vec: Improving Federated Learning by Distribution Shifts Aware Client Indexing","created":"2024-05-25","cited_by_count":0,"area":null},{"id":"2504.05140","title":"Unifying Physics- and Data-Driven Modeling via Novel Causal Spatiotemporal Graph Neural Network for Interpretable Epidemic Forecasting","created":"2025-04-07","cited_by_count":0,"area":null},{"id":"2506.02887","title":"Overcoming Challenges of Partial Client Participation in Federated Learning : A Comprehensive Review","created":"2025-06-03","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":"2510.23455","title":"SGFusion: Stochastic Geographic Gradient Fusion in Federated Learning","created":"2025-10-27","cited_by_count":0,"area":null},{"id":"2512.17986","title":"FedOAED: Federated On-Device Autoencoder Denoiser for Heterogeneous Data under Limited Client Availability","created":"2025-12-19","cited_by_count":0,"area":null},{"id":"2601.22204","title":"FedAdaVR: Adaptive Variance Reduction for Robust Federated Learning under Limited Client Participation","created":"2026-01-29","cited_by_count":0,"area":null},{"id":"2606.10124","title":"FedSteer: Taming Extreme Gradient Staleness in Federated Learning with Corrective Projections and Caching","created":"2026-06-08","cited_by_count":0,"area":null},{"id":"2606.10595","title":"From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learning","created":"2026-06-09","cited_by_count":0,"area":null}],"next_cursor":null}