{"total":38,"rows":[{"id":"2109.12273","title":"FedProc: Prototypical Contrastive Federated Learning on Non-IID data","created":"2021-09-25","cited_by_count":81,"area":null},{"id":"2111.01872","title":"Towards Fairness-Aware Federated Learning","created":"2021-11-02","cited_by_count":58,"area":null},{"id":"2111.00465","title":"DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning","created":"2021-10-31","cited_by_count":41,"area":null},{"id":"2301.01299","title":"Recent Advances on Federated Learning: A Systematic Survey","created":"2023-01-03","cited_by_count":41,"area":null},{"id":"2202.08036","title":"No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices","created":"2022-02-16","cited_by_count":37,"area":null},{"id":"2304.01950","title":"MP-FedCL: Multiprototype Federated Contrastive Learning for Edge Intelligence","created":"2023-04-01","cited_by_count":20,"area":null},{"id":"2107.00961","title":"ResIST: Layer-Wise Decomposition of ResNets for Distributed Training","created":"2021-07-02","cited_by_count":18,"area":null},{"id":"2206.02633","title":"Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity","created":"2022-05-30","cited_by_count":17,"area":null},{"id":"2208.05174","title":"FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning","created":"2022-08-10","cited_by_count":17,"area":null},{"id":"2110.03634","title":"Enabling On-Device Training of Speech Recognition Models with Federated Dropout","created":"2021-10-07","cited_by_count":12,"area":null},{"id":"2104.12416","title":"Communication-Efficient Federated Learning with Dual-Side Low-Rank Compression","created":"2021-04-26","cited_by_count":10,"area":null},{"id":"2305.09042","title":"Adaptive Federated Pruning in Hierarchical Wireless Networks","created":"2023-05-15","cited_by_count":10,"area":null},{"id":"2408.03215","title":"FedBAT: Communication-Efficient Federated Learning via Learnable Binarization","created":"2024-08-06","cited_by_count":9,"area":null},{"id":"2111.13230","title":"FedDropoutAvg: Generalizable federated learning for histopathology image classification","created":"2021-11-25","cited_by_count":7,"area":null},{"id":"2206.04385","title":"HideNseek: Federated Lottery Ticket via Server-side Pruning and Sign Supermask","created":"2022-06-09","cited_by_count":6,"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":"2505.23176","title":"The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning","created":"2025-05-29","cited_by_count":5,"area":null},{"id":"2104.06023","title":"Communication Efficient Federated Learning with Adaptive Quantization","created":"2021-04-13","cited_by_count":4,"area":null},{"id":"2206.01366","title":"Supernet Training for Federated Image Classification under System Heterogeneity","created":"2022-06-03","cited_by_count":4,"area":null},{"id":"2206.11239","title":"FedorAS: Federated Architecture Search under system heterogeneity","created":"2022-06-22","cited_by_count":4,"area":null},{"id":"2302.11485","title":"Efficient Training of Large-scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout","created":"2023-02-22","cited_by_count":4,"area":null},{"id":"2305.01163","title":"Federated Neural Radiance Fields","created":"2023-05-02","cited_by_count":4,"area":null},{"id":"2307.07172","title":"FedBIAD: Communication-Efficient and Accuracy-Guaranteed Federated Learning with Bayesian Inference-Based Adaptive Dropout","created":"2023-07-14","cited_by_count":4,"area":null},{"id":"2405.20431","title":"Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspective","created":"2024-05-30","cited_by_count":4,"area":null},{"id":"2101.07995","title":"FedNS: Improving Federated Learning for collaborative image classification on mobile clients","created":"2021-01-20","cited_by_count":3,"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":"2406.16583","title":"Personalized federated learning based on feature fusion","created":"2024-06-24","cited_by_count":2,"area":null},{"id":"2410.03042","title":"FedPeWS: Personalized Warmup via Subnetworks for Enhanced Heterogeneous Federated Learning","created":"2024-10-03","cited_by_count":2,"area":null},{"id":"2412.07216","title":"Learnable Sparse Customization in Heterogeneous Edge Computing","created":"2024-12-10","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":"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":"2311.11083","title":"ECLM: Efficient Edge-Cloud Collaborative Learning with Continuous Environment Adaptation","created":"2023-11-18","cited_by_count":1,"area":null},{"id":"2509.10161","title":"FedBiF: Communication-Efficient Federated Learning via Bits Freezing","created":"2025-09-12","cited_by_count":1,"area":null},{"id":"2201.11036","title":"Fast Server Learning Rate Tuning for Coded Federated Dropout","created":"2022-01-26","cited_by_count":0,"area":null},{"id":"2301.03720","title":"Federated Learning for Energy Constrained IoT devices: A systematic mapping study","created":"2023-01-09","cited_by_count":0,"area":null},{"id":"2503.09116","title":"Drift-Aware Federated Learning: A Causal Perspective","created":"2025-03-12","cited_by_count":0,"area":null},{"id":"2609.10404","title":"HybridFLow: SDN-Orchestrated Client Partitioning for Hybrid Federated Learning","created":"2026-09-09","cited_by_count":0,"area":null},{"id":"2610.01638","title":"FedSAP: Federated Learning with Structured Adaptive Partitioning for Multi-Domain Heterogeneous Edge Devices","created":"2026-10-01","cited_by_count":0,"area":null}],"next_cursor":null}