{"total":20,"rows":[{"id":"2401.08092","title":"A Survey of Resource-efficient LLM and Multimodal Foundation Models","created":"2024-01-16","cited_by_count":86,"area":null},{"id":"2404.15381","title":"Advances and Open Challenges in Federated Foundation Models","created":"2024-04-23","cited_by_count":36,"area":null},{"id":"2409.15723","title":"Federated Large Language Models: Current Progress and Future Directions","created":"2024-09-24","cited_by_count":27,"area":null},{"id":"2407.00113","title":"Personalized Federated Continual Learning via Multi-granularity Prompt","created":"2024-06-27","cited_by_count":20,"area":null},{"id":"2406.02224","title":"FedMKT: Federated Mutual Knowledge Transfer for Large and Small Language Models","created":"2024-06-04","cited_by_count":19,"area":null},{"id":"2406.12844","title":"Synergizing Foundation Models and Federated Learning: A Survey","created":"2024-06-18","cited_by_count":14,"area":null},{"id":"2405.20681","title":"No Free Lunch Theorem for Privacy-Preserving LLM Inference","created":"2024-05-31","cited_by_count":13,"area":null},{"id":"2412.18355","title":"Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor","created":"2024-12-24","cited_by_count":9,"area":null},{"id":"2503.02154","title":"AugFL: Augmenting Federated Learning with Pretrained Models","created":"2025-03-04","cited_by_count":6,"area":null},{"id":"2411.11707","title":"Federated Co-tuning Framework for Large and Small Language Models","created":"2024-11-18","cited_by_count":5,"area":null},{"id":"2404.12273","title":"FedEval-LLM: Federated Evaluation of Large Language Models on Downstream Tasks with Collective Wisdom","created":"2024-04-18","cited_by_count":3,"area":null},{"id":"2412.01630","title":"Review of Mathematical Optimization in Federated Learning","created":"2024-12-02","cited_by_count":3,"area":null},{"id":"2505.06907","title":"A Survey on Foundation Models for Personalized Federated Intelligence","created":"2025-05-11","cited_by_count":3,"area":null},{"id":"2507.10430","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","created":"2025-07-14","cited_by_count":2,"area":null},{"id":"2509.05142","title":"Foundational Models and Federated Learning: Survey, Taxonomy, Challenges and Practical Insights","created":"2025-09-05","cited_by_count":1,"area":null},{"id":"2506.11563","title":"A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendation","created":"2025-06-13","cited_by_count":0,"area":null},{"id":"2508.09489","title":"Large-Small Model Collaborative Framework for Federated Continual Learning","created":"2025-08-13","cited_by_count":0,"area":null},{"id":"2508.19620","title":"A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directions","created":"2025-08-27","cited_by_count":0,"area":null},{"id":"2602.12014","title":"FedGRPO: Privately Optimizing Foundation Models with Group-Relative Rewards from Domain Client","created":"2026-02-12","cited_by_count":0,"area":null},{"id":"2604.19015","title":"FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion","created":"2026-04-21","cited_by_count":0,"area":null}],"next_cursor":null}