{"total":21,"rows":[{"id":"2303.15991","title":"Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks","created":"2023-03-26","cited_by_count":95,"area":null},{"id":"2403.13101","title":"AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks","created":"2024-03-19","cited_by_count":64,"area":null},{"id":"2505.02795","title":"HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Models","created":"2025-05-05","cited_by_count":31,"area":null},{"id":"2207.09611","title":"Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directions","created":"2022-07-20","cited_by_count":14,"area":null},{"id":"2311.13348","title":"MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation","created":"2023-11-22","cited_by_count":13,"area":null},{"id":"2207.00234","title":"Visual Transformer Meets CutMix for Improved Accuracy, Communication Efficiency, and Data Privacy in Split Learning","created":"2022-07-01","cited_by_count":8,"area":null},{"id":"2412.06414","title":"Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks","created":"2024-12-09","cited_by_count":7,"area":null},{"id":"2212.14215","title":"Local Learning on Transformers via Feature Reconstruction","created":"2022-12-29","cited_by_count":4,"area":null},{"id":"2410.11577","title":"Breaking the Memory Wall for Heterogeneous Federated Learning via Model Splitting","created":"2024-10-12","cited_by_count":4,"area":null},{"id":"2303.10624","title":"PFSL: Personalized & Fair Split Learning with Data & Label Privacy for thin clients","created":"2023-03-19","cited_by_count":3,"area":null},{"id":"2304.01502","title":"SLPerf: a Unified Framework for Benchmarking Split Learning","created":"2023-04-04","cited_by_count":3,"area":null},{"id":"2407.15738","title":"Parallel Split Learning with Global Sampling","created":"2024-07-22","cited_by_count":3,"area":null},{"id":"2302.12803","title":"PiPar: Pipeline Parallelism for Collaborative Machine Learning","created":"2022-12-01","cited_by_count":2,"area":null},{"id":"2507.17228","title":"P3SL: Personalized Privacy-Preserving Split Learning on Heterogeneous Edge Devices","created":"2025-07-23","cited_by_count":2,"area":null},{"id":"2502.06355","title":"Fine-tuning Multimodal Transformers on Edge: A Parallel Split Learning Approach","created":"2025-02-10","cited_by_count":1,"area":null},{"id":"2511.18611","title":"CycleSL: Server-Client Cyclical Update Driven Scalable Split Learning","created":"2025-11-23","cited_by_count":1,"area":null},{"id":"2406.00150","title":"Non-Federated Multi-Task Split Learning for Heterogeneous Sources","created":"2024-05-31","cited_by_count":0,"area":null},{"id":"2504.07471","title":"Traversal Learning: A Lossless And Efficient Distributed Learning Framework","created":"2025-04-10","cited_by_count":0,"area":null},{"id":"2603.18540","title":"GAPSL: A Gradient-Aligned Parallel Split Learning over Data-Heterogeneous Edge Computing Systems","created":"2026-03-19","cited_by_count":0,"area":null},{"id":"2604.24468","title":"A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations","created":"2026-04-27","cited_by_count":0,"area":null},{"id":"2609.32132","title":"Latency-Aware Client Assignment for Parallel Split Learning With Global Sampling","created":"2026-09-26","cited_by_count":0,"area":null}],"next_cursor":null}