{"total":13,"rows":[{"id":"2510.27486","title":"FedAdamW: A Communication-Efficient Optimizer with Convergence and Generalization Guarantees for Federated Large Models","created":"2025-10-31","cited_by_count":15,"area":null},{"id":"2509.15087","title":"Adaptive LoRA Experts Allocation and Selection for Federated Fine-Tuning","created":"2025-09-18","cited_by_count":6,"area":null},{"id":"2407.08632","title":"Generalization Error Matters in Decentralized Learning Under Byzantine Attacks","created":"2024-07-11","cited_by_count":4,"area":null},{"id":"2504.11903","title":"FedCanon: Non-Convex Composite Federated Learning with Efficient Proximal Operation on Heterogeneous Data","created":"2025-04-16","cited_by_count":4,"area":null},{"id":"2509.16902","title":"FedEL: Federated Elastic Learning for Heterogeneous Devices","created":"2025-09-21","cited_by_count":3,"area":null},{"id":"2508.11159","title":"Mitigating Modality Quantity and Quality Imbalance in Multimodal Online Federated Learning","created":"2025-08-15","cited_by_count":2,"area":null},{"id":"2603.13282","title":"FedTreeLoRA: Reconciling Statistical and Functional Heterogeneity in Federated LoRA Fine-Tuning","created":"2026-02-27","cited_by_count":2,"area":null},{"id":"2402.02225","title":"Rethinking the Starting Point: Collaborative Pre-Training for Federated Downstream Tasks","created":"2024-02-03","cited_by_count":1,"area":null},{"id":"2405.13584","title":"Emulating Full Participation: An Effective and Fair Client Selection Strategy for Federated Learning","created":"2024-05-22","cited_by_count":1,"area":null},{"id":"2410.01209","title":"Debiasing Federated Learning with Correlated Client Participation","created":"2024-10-02","cited_by_count":1,"area":null},{"id":"2506.09438","title":"Generalization Error Analysis for Attack-Free and Byzantine-Resilient Decentralized Learning with Data Heterogeneity","created":"2025-06-11","cited_by_count":1,"area":null},{"id":"2310.05495","title":"On the Convergence of Federated Averaging under Partial Participation for Over-parameterized Neural Networks","created":"2023-10-09","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}