{"total":29,"rows":[{"id":"2208.02507","title":"ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity","created":"2022-08-04","cited_by_count":45,"area":null},{"id":"2107.12603","title":"Federated Learning Meets Natural Language Processing: A Survey","created":"2021-07-27","cited_by_count":43,"area":null},{"id":"2405.10853","title":"The Future of Large Language Model Pre-training is Federated","created":"2024-05-17","cited_by_count":30,"area":null},{"id":"2401.09135","title":"Asynchronous Local-SGD Training for Language Modeling","created":"2024-01-17","cited_by_count":26,"area":null},{"id":"2204.09715","title":"Scaling Language Model Size in Cross-Device Federated Learning","created":"2022-03-31","cited_by_count":25,"area":null},{"id":"2307.10655","title":"A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency","created":"2023-07-20","cited_by_count":21,"area":null},{"id":"2404.00836","title":"Rethinking Resource Management in Edge Learning: A Joint Pre-training and Fine-tuning Design Paradigm","created":"2024-04-01","cited_by_count":13,"area":null},{"id":"2205.13299","title":"Federated Split BERT for Heterogeneous Text Classification","created":"2022-05-26","cited_by_count":11,"area":null},{"id":"2205.13722","title":"Can Foundation Models Help Us Achieve Perfect Secrecy?","created":"2022-05-27","cited_by_count":9,"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":"2407.09690","title":"Private Heterogeneous Federated Learning Without a Trusted Server Revisited: Error-Optimal and Communication-Efficient Algorithms for Convex Losses","created":"2024-07-12","cited_by_count":8,"area":null},{"id":"2501.04436","title":"Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions","created":"2025-01-08","cited_by_count":8,"area":null},{"id":"2410.03529","title":"No Need to Talk: Asynchronous Mixture of Language Models","created":"2024-10-04","cited_by_count":5,"area":null},{"id":"2408.15600","title":"Exploring Selective Layer Fine-Tuning in Federated Learning","created":"2024-08-28","cited_by_count":3,"area":null},{"id":"2305.03584","title":"Now It Sounds Like You: Learning Personalized Vocabulary On Device","created":"2023-05-05","cited_by_count":2,"area":null},{"id":"2403.08100","title":"Efficient Language Model Architectures for Differentially Private Federated Learning","created":"2024-03-12","cited_by_count":2,"area":null},{"id":"2411.03799","title":"Overcoming label shift with target-aware federated learning","created":"2024-11-06","cited_by_count":2,"area":null},{"id":"2412.04650","title":"Revisiting Federated Fine-Tuning: A Single Communication Round is Enough for Foundation Models","created":"2024-12-05","cited_by_count":2,"area":null},{"id":"2501.12537","title":"Enhancing Privacy in the Early Detection of Sexual Predators Through Federated Learning and Differential Privacy","created":"2025-01-21","cited_by_count":2,"area":null},{"id":"2411.16462","title":"Lion Cub: Minimizing Communication Overhead in Distributed Lion","created":"2024-11-25","cited_by_count":1,"area":null},{"id":"2503.04052","title":"The Impact Analysis of Delays in Asynchronous Federated Learning with Data Heterogeneity for Edge Intelligence","created":"2025-03-06","cited_by_count":1,"area":null},{"id":"2301.10400","title":"When to Trust Aggregated Gradients: Addressing Negative Client Sampling in Federated Learning","created":"2023-01-25","cited_by_count":0,"area":null},{"id":"2407.20557","title":"CELLM: An Efficient Communication in Large Language Models Training for Federated Learning","created":"2024-07-30","cited_by_count":0,"area":null},{"id":"2412.17692","title":"FedTLU: Federated Learning with Targeted Layer Updates","created":"2024-12-23","cited_by_count":0,"area":null},{"id":"2503.15390","title":"FedSCA: Federated Tuning with Similarity-guided Collaborative Aggregation for Heterogeneous Medical Image Segmentation","created":"2025-03-19","cited_by_count":0,"area":null},{"id":"2506.00743","title":"Assortment of Attention Heads: Accelerating Federated PEFT with Head Pruning and Strategic Client Selection","created":"2025-05-31","cited_by_count":0,"area":null},{"id":"2511.12188","title":"Scaling Law Analysis in Federated Learning: How to Select the Optimal Model Size?","created":"2025-11-15","cited_by_count":0,"area":null},{"id":"2603.24242","title":"Optimizing Multilingual LLMs via Federated Learning: A Study of Client Language Composition","created":"2026-03-25","cited_by_count":0,"area":null},{"id":"2605.13936","title":"Towards the Next Frontier of LLMs, Training on Private Data: A Cross-Domain Benchmark for Federated Fine-Tuning","created":"2026-05-13","cited_by_count":0,"area":null}],"next_cursor":null}