{"total":23,"rows":[{"id":"2406.02958","title":"PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs","created":"2024-06-05","cited_by_count":29,"area":null},{"id":"2308.13894","title":"FwdLLM: Efficient FedLLM using Forward Gradient","created":"2023-08-26","cited_by_count":26,"area":null},{"id":"2308.14363","title":"Mobile Foundation Model as Firmware","created":"2023-08-28","cited_by_count":25,"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":"2506.01001","title":"FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning","created":"2025-06-01","cited_by_count":10,"area":null},{"id":"2310.04627","title":"Profit: Benchmarking Personalization and Robustness Trade-off in Federated Prompt Tuning","created":"2023-10-06","cited_by_count":9,"area":null},{"id":"2310.05143","title":"ZooPFL: Exploring Black-box Foundation Models for Personalized Federated Learning","created":"2023-10-08","cited_by_count":9,"area":null},{"id":"2502.20639","title":"FedConv: A Learning-on-Model Paradigm for Heterogeneous Federated Clients","created":"2025-02-28","cited_by_count":7,"area":null},{"id":"2506.05977","title":"Mitigating Catastrophic Forgetting with Adaptive Transformer Block Expansion in Federated Fine-Tuning","created":"2025-06-06","cited_by_count":7,"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":"2405.09394","title":"SPD-CFL: Stepwise Parameter Dropout for Efficient Continual Federated Learning","created":"2024-05-15","cited_by_count":5,"area":null},{"id":"2412.20004","title":"Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices","created":"2024-12-28","cited_by_count":5,"area":null},{"id":"2406.11187","title":"Save It All: Enabling Full Parameter Tuning for Federated Large Language Models via Cycle Block Gradient Descent","created":"2024-06-17","cited_by_count":4,"area":null},{"id":"2503.21213","title":"Resource-Efficient Federated Fine-Tuning Large Language Models for Heterogeneous Data","created":"2025-03-27","cited_by_count":4,"area":null},{"id":"2508.00041","title":"Learning Like Humans: Resource-Efficient Federated Fine-Tuning through Cognitive Developmental Stages","created":"2025-07-31","cited_by_count":4,"area":null},{"id":"2410.14852","title":"FedSpaLLM: Federated Pruning of Large Language Models","created":"2024-10-18","cited_by_count":3,"area":null},{"id":"2310.17491","title":"FedPEAT: Convergence of Federated Learning, Parameter-Efficient Fine Tuning, and Emulator Assisted Tuning for Artificial Intelligence Foundation Models with Mobile Edge Computing","created":"2023-10-26","cited_by_count":2,"area":null},{"id":"2503.05474","title":"Personalized Federated Learning via Learning Dynamic Graphs","created":"2025-03-07","cited_by_count":2,"area":null},{"id":"2507.05852","title":"Prototype-Guided and Lightweight Adapters for Inherent Interpretation and Generalisation in Federated Learning","created":"2025-07-08","cited_by_count":0,"area":null},{"id":"2511.05028","title":"OvA-LP: A Simple and Efficient Framework for Federated Learning on Non-IID Data","created":"2025-11-07","cited_by_count":0,"area":null},{"id":"2511.06757","title":"Implicit Federated In-context Learning For Task-Specific LLM Fine-Tuning","created":"2025-11-10","cited_by_count":0,"area":null},{"id":"2604.06297","title":"FedSpy-LLM: Towards Scalable and Generalizable Data Reconstruction Attacks from Gradients on LLMs","created":"2026-04-07","cited_by_count":0,"area":null},{"id":"2609.05403","title":"RegionFed: Federated Learning for Personalized Query Understanding in Heterogeneous Retail Environments","created":"2026-09-04","cited_by_count":0,"area":null}],"next_cursor":null}