{"total":17,"rows":[{"id":"2302.05601","title":"Pruning Deep Neural Networks from a Sparsity Perspective","created":"2023-02-11","cited_by_count":25,"area":null},{"id":"2302.02573","title":"Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey","created":"2023-02-06","cited_by_count":23,"area":null},{"id":"2204.08069","title":"Self-Aware Personalized Federated Learning","created":"2022-04-17","cited_by_count":16,"area":null},{"id":"2307.02623","title":"FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout","created":"2023-07-05","cited_by_count":16,"area":null},{"id":"2310.10780","title":"Demystifying Poisoning Backdoor Attacks from a Statistical Perspective","created":"2023-10-16","cited_by_count":10,"area":null},{"id":"2203.08176","title":"SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge Intelligence","created":"2022-03-15","cited_by_count":7,"area":null},{"id":"2309.08173","title":"FedJudge: Federated Legal Large Language Model","created":"2023-09-15","cited_by_count":5,"area":null},{"id":"2205.09470","title":"Nebula-I: A General Framework for Collaboratively Training Deep Learning Models on Low-Bandwidth Cloud Clusters","created":"2022-05-19","cited_by_count":3,"area":null},{"id":"2507.19822","title":"Debunking Optimization Myths in Federated Learning for Medical Image Classification","created":"2025-07-26","cited_by_count":3,"area":null},{"id":"2305.17052","title":"A Framework for Incentivized Collaborative Learning","created":"2023-05-26","cited_by_count":2,"area":null},{"id":"2210.15977","title":"FedVMR: A New Federated Learning method for Video Moment Retrieval","created":"2022-10-28","cited_by_count":1,"area":null},{"id":"2304.11524","title":"Personalized Federated Learning via Gradient Modulation for Heterogeneous Text Summarization","created":"2023-04-23","cited_by_count":1,"area":null},{"id":"2409.04986","title":"DynamicFL: Federated Learning with Dynamic Communication Resource Allocation","created":"2024-09-08","cited_by_count":1,"area":null},{"id":"2207.00392","title":"Better Methods and Theory for Federated Learning: Compression, Client Selection and Heterogeneity","created":"2022-07-01","cited_by_count":0,"area":null},{"id":"2208.14808","title":"Reducing Impacts of System Heterogeneity in Federated Learning using Weight Update Magnitudes","created":"2022-08-30","cited_by_count":0,"area":null},{"id":"2410.23824","title":"Generative AI-Powered Plugin for Robust Federated Learning in Heterogeneous IoT Networks","created":"2024-10-31","cited_by_count":0,"area":null},{"id":"2603.12684","title":"Federated Hierarchical Clustering with Automatic Selection of Optimal Cluster Numbers","created":"2026-03-13","cited_by_count":0,"area":null}],"next_cursor":null}