{"total":27,"rows":[{"id":"2303.00566","title":"Structured Pruning for Deep Convolutional Neural Networks: A survey","created":"2023-03-01","cited_by_count":122,"area":null},{"id":"2310.15080","title":"Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization","created":"2023-10-23","cited_by_count":65,"area":null},{"id":"2207.07223","title":"Accelerated Federated Learning with Decoupled Adaptive Optimization","created":"2022-07-14","cited_by_count":23,"area":null},{"id":"2206.10546","title":"FedHiSyn: A Hierarchical Synchronous Federated Learning Framework for Resource and Data Heterogeneity","created":"2022-06-21","cited_by_count":20,"area":null},{"id":"2211.13430","title":"Multi-Job Intelligent Scheduling with Cross-Device Federated Learning","created":"2022-11-24","cited_by_count":20,"area":null},{"id":"2312.10935","title":"AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devices","created":"2023-12-18","cited_by_count":15,"area":null},{"id":"2411.01583","title":"Trustworthy Federated Learning: Privacy, Security, and Beyond","created":"2024-11-03","cited_by_count":11,"area":null},{"id":"2312.15186","title":"Efficient Asynchronous Federated Learning with Sparsification and Quantization","created":"2023-12-23","cited_by_count":9,"area":null},{"id":"2403.19178","title":"Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning","created":"2024-03-28","cited_by_count":9,"area":null},{"id":"2408.05678","title":"Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data","created":"2024-08-11","cited_by_count":6,"area":null},{"id":"2410.00131","title":"Fisher Information-based Efficient Curriculum Federated Learning with Large Language Models","created":"2024-09-30","cited_by_count":6,"area":null},{"id":"2304.05871","title":"Edge-cloud Collaborative Learning with Federated and Centralized Features","created":"2023-04-12","cited_by_count":5,"area":null},{"id":"2308.16835","title":"FedDD: Toward Communication-efficient Federated Learning with Differential Parameter Dropout","created":"2023-08-31","cited_by_count":2,"area":null},{"id":"2312.08975","title":"On Mask-based Image Set Desensitization with Recognition Support","created":"2023-12-14","cited_by_count":2,"area":null},{"id":"2405.03248","title":"Communication-Efficient Federated Learning with Adaptive Compression under Dynamic Bandwidth","created":"2024-05-06","cited_by_count":2,"area":null},{"id":"2507.06031","title":"Efficient Federated Learning with Timely Update Dissemination","created":"2025-07-08","cited_by_count":2,"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":"2404.13575","title":"FedMPQ: Secure and Communication-Efficient Federated Learning with Multi-codebook Product Quantization","created":"2024-04-21","cited_by_count":1,"area":null},{"id":"2408.09762","title":"Sequential Federated Learning in Hierarchical Architecture on Non-IID Datasets","created":"2024-08-19","cited_by_count":1,"area":null},{"id":"2411.01759","title":"Automatic Structured Pruning for Efficient Architecture in Federated Learning","created":"2024-11-04","cited_by_count":1,"area":null},{"id":"2303.00179","title":"A Unified Momentum-based Paradigm of Decentralized SGD for Non-Convex Models and Heterogeneous Data","created":"2023-03-01","cited_by_count":0,"area":null},{"id":"2312.11905","title":"Convergence Visualizer of Decentralized Federated Distillation with Reduced Communication Costs","created":"2023-12-19","cited_by_count":0,"area":null},{"id":"2405.15788","title":"Towards Fairness in Provably Communication-Efficient Federated Recommender Systems","created":"2024-05-03","cited_by_count":0,"area":null},{"id":"2503.05755","title":"SEAFL: Enhancing Efficiency in Semi-Asynchronous Federated Learning through Adaptive Aggregation and Selective Training","created":"2025-02-22","cited_by_count":0,"area":null},{"id":"2507.04327","title":"TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments","created":"2025-07-06","cited_by_count":0,"area":null},{"id":"2509.08750","title":"PracMHBench: Re-evaluating Model-Heterogeneous Federated Learning Based on Practical Edge Device Constraints","created":"2025-09-04","cited_by_count":0,"area":null},{"id":"2609.19042","title":"FedASAP: Activation Statistics-driven Structured Adaptive Pruning for Efficient Personalized Federated Learning for Lesion Segmentation on brain MRI","created":"2026-07-20","cited_by_count":0,"area":null}],"next_cursor":null}