{"total":18,"rows":[{"id":"2501.00365","title":"Low-Rank Adaptation for Foundation Models: A Comprehensive Review","created":"2024-12-31","cited_by_count":37,"area":null},{"id":"2504.21099","title":"A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning","created":"2025-04-29","cited_by_count":17,"area":null},{"id":"2501.15361","title":"Decentralized Low-Rank Fine-Tuning of Large Language Models","created":"2025-01-26","cited_by_count":14,"area":null},{"id":"2506.02961","title":"FlowerTune: A Cross-Domain Benchmark for Federated Fine-Tuning of Large Language Models","created":"2025-06-03","cited_by_count":11,"area":null},{"id":"2505.13502","title":"Federated Low-Rank Adaptation for Foundation Models: A Survey","created":"2025-05-16","cited_by_count":9,"area":null},{"id":"2412.14326","title":"Covariances for Free: Exploiting Mean Distributions for Training-free Federated Learning","created":"2024-12-18","cited_by_count":4,"area":null},{"id":"2501.19389","title":"Federated Sketching LoRA: A Flexible Framework for Heterogeneous Collaborative Fine-Tuning of LLMs","created":"2025-01-31","cited_by_count":2,"area":null},{"id":"2508.01348","title":"Convergence Analysis of Aggregation-Broadcast in LoRA-enabled Distributed Fine-Tuning","created":"2025-08-02","cited_by_count":2,"area":null},{"id":"2505.23555","title":"Adaptive Federated LoRA in Heterogeneous Wireless Networks with Independent Sampling","created":"2025-05-29","cited_by_count":1,"area":null},{"id":"2601.21898","title":"Making Models Unmergeable via Scaling-Sensitive Loss Landscape","created":"2026-01-29","cited_by_count":1,"area":null},{"id":"2602.01126","title":"WinFLoRA: Incentivizing Client-Adaptive Aggregation in Federated LoRA under Privacy Heterogeneity","created":"2026-02-01","cited_by_count":1,"area":null},{"id":"2605.05769","title":"Adaptive Selection of LoRA Components in Privacy-Preserving Federated Learning","created":"2026-05-07","cited_by_count":1,"area":null},{"id":"2608.10144","title":"SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks","created":"2026-08-10","cited_by_count":1,"area":null},{"id":"2510.25181","title":"Fed-PELAD: Communication-Efficient Federated Learning for Massive MIMO CSI Feedback with Personalized Encoders and a LoRA-Adapted Shared Decoder","created":"2025-10-29","cited_by_count":0,"area":null},{"id":"2605.11857","title":"Beyond Parameter Aggregation: Semantic Consensus for Federated Fine-Tuning of LLMs","created":"2026-05-12","cited_by_count":0,"area":null},{"id":"2607.14367","title":"Dysco: Dynamic Subspace Boosting to Mitigate LoRA Interference in Federated Learning","created":"2026-07-15","cited_by_count":0,"area":null},{"id":"2608.03605","title":"FraQ: Efficient Coordinate-Space Recompression for Federated Low-Rank Adaptation","created":"2026-08-04","cited_by_count":0,"area":null},{"id":"2609.33147","title":"CFLoRA: Federated Fine-tuning of LLMs with Complementary Factors for Error-free Aggregation","created":"2026-09-27","cited_by_count":0,"area":null}],"next_cursor":null}