{"total":95,"rows":[{"id":"2308.06522","title":"SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models","created":"2023-08-12","cited_by_count":93,"area":null},{"id":"2303.13137","title":"FedGH: Heterogeneous Federated Learning with Generalized Global Header","created":"2023-03-23","cited_by_count":56,"area":null},{"id":"2307.09182","title":"Federated Learning for Computationally-Constrained Heterogeneous Devices: A Survey","created":"2023-07-18","cited_by_count":40,"area":null},{"id":"2310.13283","title":"pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning","created":"2023-10-20","cited_by_count":40,"area":null},{"id":"2304.06947","title":"TimelyFL: Heterogeneity-aware Asynchronous Federated Learning with Adaptive Partial Training","created":"2023-04-14","cited_by_count":27,"area":null},{"id":"2306.02210","title":"GPT-FL: Generative Pre-trained Model-Assisted Federated Learning","created":"2023-06-03","cited_by_count":24,"area":null},{"id":"2309.13546","title":"DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning","created":"2023-09-24","cited_by_count":24,"area":null},{"id":"2310.08670","title":"Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction","created":"2023-10-12","cited_by_count":24,"area":null},{"id":"2406.00488","title":"Federated Model Heterogeneous Matryoshka Representation Learning","created":"2024-06-01","cited_by_count":21,"area":null},{"id":"2407.19389","title":"FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extraction","created":"2024-07-28","cited_by_count":21,"area":null},{"id":"2308.11841","title":"A Survey for Federated Learning Evaluations: Goals and Measures","created":"2023-08-23","cited_by_count":16,"area":null},{"id":"2305.10730","title":"Is Aggregation the Only Choice? Federated Learning via Layer-wise Model Recombination","created":"2023-05-18","cited_by_count":13,"area":null},{"id":"2310.09650","title":"Multimodal Federated Learning in Healthcare: a Review","created":"2023-10-14","cited_by_count":13,"area":null},{"id":"2404.09816","title":"FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity","created":"2024-04-15","cited_by_count":13,"area":null},{"id":"2405.09839","title":"Advances in Robust Federated Learning: A Survey with Heterogeneity Considerations","created":"2024-05-16","cited_by_count":12,"area":null},{"id":"2312.11489","title":"Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration","created":"2023-12-01","cited_by_count":10,"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":"2311.06879","title":"pFedES: Model Heterogeneous Personalized Federated Learning with Feature Extractor Sharing","created":"2023-11-12","cited_by_count":9,"area":null},{"id":"2407.03086","title":"Effective Heterogeneous Federated Learning via Efficient Hypernetwork-based Weight Generation","created":"2024-07-03","cited_by_count":9,"area":null},{"id":"2412.05823","title":"DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices","created":"2024-12-08","cited_by_count":9,"area":null},{"id":"2308.14929","title":"Maestro: Uncovering Low-Rank Structures via Trainable Decomposition","created":"2023-08-28","cited_by_count":8,"area":null},{"id":"2310.00109","title":"FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of Things","created":"2023-09-29","cited_by_count":8,"area":null},{"id":"2403.01387","title":"A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications","created":"2024-03-03","cited_by_count":8,"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":"2502.20639","title":"FedConv: A Learning-on-Model Paradigm for Heterogeneous Federated Clients","created":"2025-02-28","cited_by_count":7,"area":null},{"id":"2306.16484","title":"Towards a Better Theoretical Understanding of Independent Subnetwork Training","created":"2023-06-28","cited_by_count":6,"area":null},{"id":"2310.17761","title":"Distributed Personalized Empirical Risk Minimization","created":"2023-10-26","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":"2410.23111","title":"Exploring Gradient Subspaces: Addressing and Overcoming LoRA's Limitations in Federated Fine-Tuning of Large Language Models","created":"2024-10-30","cited_by_count":6,"area":null},{"id":"2305.13170","title":"Explicit Personalization and Local Training: Double Communication Acceleration in Federated Learning","created":"2023-05-22","cited_by_count":5,"area":null},{"id":"2308.11464","title":"Internal Cross-layer Gradients for Extending Homogeneity to Heterogeneity in Federated Learning","created":"2023-08-22","cited_by_count":5,"area":null},{"id":"2409.18461","title":"Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge Integration","created":"2024-09-27","cited_by_count":5,"area":null},{"id":"2309.03237","title":"Federated Learning Over Images: Vertical Decompositions and Pre-Trained Backbones Are Difficult to Beat","created":"2023-09-06","cited_by_count":4,"area":null},{"id":"2402.01863","title":"DFML: Decentralized Federated Mutual Learning","created":"2024-02-02","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":"2404.17847","title":"pFedAFM: Adaptive Feature Mixture for Batch-Level Personalization in Heterogeneous Federated Learning","created":"2024-04-27","cited_by_count":3,"area":null},{"id":"2410.08457","title":"Unity is Power: Semi-Asynchronous Collaborative Training of Large-Scale Models with Structured Pruning in Resource-Limited Clients","created":"2024-10-11","cited_by_count":3,"area":null},{"id":"2503.02549","title":"Federated nnU-Net for Privacy-Preserving Medical Image Segmentation","created":"2025-03-04","cited_by_count":3,"area":null},{"id":"2504.07635","title":"Generative Artificial Intelligence for Internet of Things Computing: A Systematic Survey","created":"2025-04-10","cited_by_count":3,"area":null},{"id":"2507.09029","title":"Model Parallelism With Subnetwork Data Parallelism","created":"2025-07-11","cited_by_count":3,"area":null},{"id":"2509.16902","title":"FedEL: Federated Elastic Learning for Heterogeneous Devices","created":"2025-09-21","cited_by_count":3,"area":null},{"id":"2311.10002","title":"Straggler-resilient Federated Learning: Tackling Computation Heterogeneity with Layer-wise Partial Model Training in Mobile Edge Network","created":"2023-11-16","cited_by_count":2,"area":null},{"id":"2402.17202","title":"FedBRB: An Effective Solution to the Small-to-Large Scenario in Device-Heterogeneity Federated Learning","created":"2024-02-27","cited_by_count":2,"area":null},{"id":"2408.09556","title":"Addressing Heterogeneity in Federated Learning: Challenges and Solutions for a Shared Production Environment","created":"2024-08-18","cited_by_count":2,"area":null},{"id":"2409.07734","title":"DFDG: Data-Free Dual-Generator Adversarial Distillation for One-Shot Federated Learning","created":"2024-09-12","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":"2412.00334","title":"EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients","created":"2024-11-30","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":"2504.15300","title":"Collaborative Learning of On-Device Small Model and Cloud-Based Large Model: Advances and Future Directions","created":"2025-04-17","cited_by_count":2,"area":null},{"id":"2602.01626","title":"Toward Enhancing Representation Learning in Federated Multi-Task Settings","created":"2026-02-02","cited_by_count":2,"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":"2404.13515","title":"FedTrans: Efficient Federated Learning via Multi-Model Transformation","created":"2024-04-21","cited_by_count":1,"area":null},{"id":"2405.13378","title":"FedCache 2.0: Federated Edge Learning with Knowledge Caching and Dataset Distillation","created":"2024-05-22","cited_by_count":1,"area":null},{"id":"2410.18287","title":"LEGO: Language Model Building Blocks","created":"2024-10-23","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":"2501.00693","title":"Beyond Model Scale Limits: End-Edge-Cloud Federated Learning with Self-Rectified Knowledge Agglomeration","created":"2025-01-01","cited_by_count":1,"area":null},{"id":"2502.08518","title":"FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices","created":"2025-02-12","cited_by_count":1,"area":null},{"id":"2502.15940","title":"Orthogonal Calibration for Asynchronous Federated Learning","created":"2025-02-21","cited_by_count":1,"area":null},{"id":"2507.22330","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","created":"2025-07-30","cited_by_count":1,"area":null},{"id":"2508.01669","title":"Bridging Generalization Gap of Heterogeneous Federated Clients Using Generative Models","created":"2025-08-03","cited_by_count":1,"area":null},{"id":"2508.03579","title":"Heterogeneity-Oblivious Robust Federated Learning","created":"2025-08-05","cited_by_count":1,"area":null},{"id":"2510.11400","title":"FedHybrid: Breaking the Memory Wall of Federated Learning via Hybrid Tensor Management","created":"2025-10-13","cited_by_count":1,"area":null},{"id":"2511.06132","title":"On the Convergence and Stability of Distributed Sub-model Training","created":"2025-11-08","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":"2604.06631","title":"SubFLOT: Submodel Extraction for Efficient and Personalized Federated Learning via Optimal Transport","created":"2026-04-08","cited_by_count":1,"area":null},{"id":"2306.09384","title":"MobileASR: A resource-aware on-device learning framework for user voice personalization applications on mobile phones","created":"2023-06-15","cited_by_count":0,"area":null},{"id":"2312.04281","title":"Factor-Assisted Federated Learning for Personalized Optimization with Heterogeneous Data","created":"2023-12-07","cited_by_count":0,"area":null},{"id":"2312.17159","title":"Replica Tree-based Federated Learning using Limited Data","created":"2023-12-28","cited_by_count":0,"area":null},{"id":"2402.02268","title":"Federated Learning with New Knowledge: Fundamentals, Advances, and Futures","created":"2024-02-03","cited_by_count":0,"area":null},{"id":"2402.15968","title":"CoDream: Exchanging dreams instead of models for federated aggregation with heterogeneous models","created":"2024-02-25","cited_by_count":0,"area":null},{"id":"2405.00885","title":"WHALE-FL: Wireless and Heterogeneity Aware Latency Efficient Federated Learning over Mobile Devices via Adaptive Subnetwork Scheduling","created":"2024-05-01","cited_by_count":0,"area":null},{"id":"2405.20589","title":"Selective Knowledge Sharing for Personalized Federated Learning Under Capacity Heterogeneity","created":"2024-05-31","cited_by_count":0,"area":null},{"id":"2407.00943","title":"FedEx: Expediting Federated Learning over Heterogeneous Mobile Devices by Overlapping and Participant Selection","created":"2024-07-01","cited_by_count":0,"area":null},{"id":"2407.03622","title":"MSfusion: A Dynamic Model Splitting Approach for Resource-Constrained Machines to Collaboratively Train Larger Models","created":"2024-07-04","cited_by_count":0,"area":null},{"id":"2407.05098","title":"FedTSA: A Cluster-based Two-Stage Aggregation Method for Model-heterogeneous Federated Learning","created":"2024-07-06","cited_by_count":0,"area":null},{"id":"2409.08372","title":"FedProphet: Memory-Efficient Federated Adversarial Training via Robust and Consistent Cascade Learning","created":"2024-09-12","cited_by_count":0,"area":null},{"id":"2410.24106","title":"On Sampling Strategies for Spectral Model Sharding","created":"2024-10-31","cited_by_count":0,"area":null},{"id":"2501.00170","title":"Federated Learning with Workload Reduction through Partial Training of Client Models and Entropy-Based Data Selection","created":"2024-12-30","cited_by_count":0,"area":null},{"id":"2501.12624","title":"Knowledge-Driven Federated Graph Learning on Model Heterogeneity","created":"2025-01-22","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},{"id":"2504.00726","title":"EMO: Edge Model Overlays to Scale Model Size in Federated Learning","created":"2025-04-01","cited_by_count":0,"area":null},{"id":"2504.01752","title":"A Two-Timescale Approach for Wireless Federated Learning with Parameter Freezing and Power Control","created":"2025-04-02","cited_by_count":0,"area":null},{"id":"2506.05411","title":"QA-HFL: Quality-Aware Hierarchical Federated Learning for Resource-Constrained Mobile Devices with Heterogeneous Image Quality","created":"2025-06-04","cited_by_count":0,"area":null},{"id":"2507.04310","title":"Heterogeneous Federated Learning with Prototype Alignment and Upscaling","created":"2025-07-06","cited_by_count":0,"area":null},{"id":"2508.08552","title":"SHEFL: Sparse Heterogeneous Ensemble Federated Learning with Group-Balanced Aggregation","created":"2025-08-12","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":"2604.11278","title":"Representation-Aligned Multi-Scale Personalization for Federated Learning","created":"2026-04-13","cited_by_count":0,"area":null},{"id":"2604.23386","title":"A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning","created":"2026-04-25","cited_by_count":0,"area":null},{"id":"2605.02337","title":"FedPLT: Scalable, Resource-Efficient, and Heterogeneity-Aware Federated Learning via Partial Layer Training","created":"2026-05-04","cited_by_count":0,"area":null},{"id":"2605.05959","title":"From Coordinate Matching to Structural Alignment: Rethinking Prototype Alignment in Heterogeneous Federated Learning","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2606.07621","title":"HASA: Subnet Allocation for Compute-Constrained Model-Heterogeneous Federated Learning","created":"2026-05-30","cited_by_count":0,"area":null},{"id":"2607.29071","title":"Federated Foundation Models Fine-Tuning with Heterogeneous Compressed Clients","created":"2026-07-31","cited_by_count":0,"area":null},{"id":"2608.07157","title":"Capacity Confounds and Coverage Guarantees in Adaptive Sub-model Federated Learning","created":"2026-08-07","cited_by_count":0,"area":null},{"id":"2609.06106","title":"FANS: Federated Adaptive Network Search Learning for Heterogeneous Devices","created":"2026-09-05","cited_by_count":0,"area":null},{"id":"2610.01718","title":"vFedProtoQNAS: Prototype-Guided Personalized Quantum Neural Architecture Search for Virtual Federated Learning","created":"2026-10-01","cited_by_count":0,"area":null}],"next_cursor":null}