{"total":26,"rows":[{"id":"2307.10616","title":"Heterogeneous Federated Learning: State-of-the-art and Research Challenges","created":"2023-07-20","cited_by_count":161,"area":null},{"id":"2211.08025","title":"FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers","created":"2022-11-15","cited_by_count":33,"area":null},{"id":"2207.07223","title":"Accelerated Federated Learning with Decoupled Adaptive Optimization","created":"2022-07-14","cited_by_count":23,"area":null},{"id":"2308.13558","title":"Federated Learning for Computer Vision","created":"2023-08-24","cited_by_count":22,"area":null},{"id":"2301.12195","title":"BAFFLE: A Baseline of Backpropagation-Free Federated Learning","created":"2023-01-28","cited_by_count":12,"area":null},{"id":"2304.08928","title":"ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees","created":"2023-04-18","cited_by_count":12,"area":null},{"id":"2404.13349","title":"Breaking the Memory Wall for Heterogeneous Federated Learning via Progressive Training","created":"2024-04-20","cited_by_count":10,"area":null},{"id":"2305.17005","title":"Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices","created":"2023-05-26","cited_by_count":7,"area":null},{"id":"2212.04084","title":"Federated Learning for Inference at Anytime and Anywhere","created":"2022-12-08","cited_by_count":6,"area":null},{"id":"2410.11559","title":"Why Go Full? Elevating Federated Learning Through Partial Network Updates","created":"2024-10-15","cited_by_count":6,"area":null},{"id":"2502.04850","title":"Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks","created":"2025-02-07","cited_by_count":6,"area":null},{"id":"2302.01068","title":"FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations","created":"2023-02-02","cited_by_count":5,"area":null},{"id":"2409.17836","title":"Language Models as Zero-shot Lossless Gradient Compressors: Towards General Neural Parameter Prior Models","created":"2024-09-26","cited_by_count":5,"area":null},{"id":"2503.17070","title":"A Thorough Assessment of the Non-IID Data Impact in Federated Learning","created":"2025-03-21","cited_by_count":5,"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":"2306.05172","title":"FLEdge: Benchmarking Federated Machine Learning Applications in Edge Computing Systems","created":"2023-06-08","cited_by_count":3,"area":null},{"id":"2311.09270","title":"FedCode: Communication-Efficient Federated Learning via Transferring Codebooks","created":"2023-11-15","cited_by_count":3,"area":null},{"id":"2306.03626","title":"Understanding Progressive Training Through the Framework of Randomized Coordinate Descent","created":"2023-06-06","cited_by_count":2,"area":null},{"id":"2207.09568","title":"FedNet2Net: Saving Communication and Computations in Federated Learning with Model Growing","created":"2022-07-19","cited_by_count":1,"area":null},{"id":"2311.15617","title":"VeryFL: A Verify Federated Learning Framework Embedded with Blockchain","created":"2023-11-27","cited_by_count":1,"area":null},{"id":"2407.15426","title":"Resource-Efficient Federated Multimodal Learning via Layer-wise and Progressive Training","created":"2024-07-22","cited_by_count":1,"area":null},{"id":"2411.07826","title":"Efficient Federated Finetuning of Tiny Transformers with Resource-Constrained Devices","created":"2024-11-12","cited_by_count":1,"area":null},{"id":"2301.06768","title":"FedCliP: Federated Learning with Client Pruning","created":"2023-01-17","cited_by_count":0,"area":null},{"id":"2312.15966","title":"Federated Hyperdimensional Computing","created":"2023-12-26","cited_by_count":0,"area":null},{"id":"2401.11647","title":"Resource-efficient Layer-wise Federated Self-supervised Learning","created":"2024-01-22","cited_by_count":0,"area":null},{"id":"2502.10239","title":"Efficient Zeroth-Order Federated Finetuning of Language Models on Resource-Constrained Devices","created":"2025-02-14","cited_by_count":0,"area":null}],"next_cursor":null}