{"total":337,"rows":[{"id":"2103.00710","title":"Towards Personalized Federated Learning","created":"2021-03-01","cited_by_count":464,"area":null},{"id":"2103.07853","title":"Membership Inference Attacks on Machine Learning: A Survey","created":"2021-03-14","cited_by_count":321,"area":"safety"},{"id":"2305.05644","title":"Towards Building the Federated GPT: Federated Instruction Tuning","created":"2023-05-09","cited_by_count":185,"area":null},{"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":"2108.06098","title":"FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated Learning","created":"2021-08-13","cited_by_count":141,"area":null},{"id":"2111.02862","title":"Parameterized Knowledge Transfer for Personalized Federated Learning","created":"2021-11-04","cited_by_count":109,"area":null},{"id":"2212.01548","title":"FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction","created":"2022-12-03","cited_by_count":102,"area":null},{"id":"2308.06522","title":"SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models","created":"2023-08-12","cited_by_count":93,"area":null},{"id":"2402.11505","title":"Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources","created":"2024-02-18","cited_by_count":93,"area":null},{"id":"2102.03448","title":"Federated Reconstruction: Partially Local Federated Learning","created":"2021-02-05","cited_by_count":79,"area":null},{"id":"2206.00187","title":"DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training","created":"2022-06-01","cited_by_count":74,"area":null},{"id":"2102.04761","title":"Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data","created":"2021-02-09","cited_by_count":69,"area":null},{"id":"2110.14216","title":"What Do We Mean by Generalization in Federated Learning?","created":"2021-10-27","cited_by_count":62,"area":null},{"id":"2111.09360","title":"Personalized Federated Learning through Local Memorization","created":"2021-11-17","cited_by_count":57,"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":"2111.07494","title":"Federated Learning for Internet of Things: Applications, Challenges, and Opportunities","created":"2021-11-15","cited_by_count":50,"area":null},{"id":"2401.03230","title":"FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning","created":"2024-01-06","cited_by_count":47,"area":null},{"id":"2201.01601","title":"FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clients","created":"2022-01-05","cited_by_count":46,"area":null},{"id":"2106.01432","title":"SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training","created":"2021-06-02","cited_by_count":45,"area":null},{"id":"2206.03655","title":"pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning","created":"2022-06-08","cited_by_count":44,"area":null},{"id":"2210.04505","title":"A Survey on Heterogeneous Federated Learning","created":"2022-10-10","cited_by_count":42,"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":"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":"2205.10920","title":"Test-Time Robust Personalization for Federated Learning","created":"2022-05-22","cited_by_count":39,"area":null},{"id":"2111.03741","title":"Sharp Bounds for Federated Averaging (Local SGD) and Continuous Perspective","created":"2021-11-05","cited_by_count":38,"area":null},{"id":"2305.02776","title":"Efficient Personalized Federated Learning via Sparse Model-Adaptation","created":"2023-05-04","cited_by_count":38,"area":null},{"id":"2311.01483","title":"FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networks","created":"2023-11-02","cited_by_count":38,"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":"2110.10302","title":"Layer-wise Adaptive Model Aggregation for Scalable Federated Learning","created":"2021-10-19","cited_by_count":36,"area":null},{"id":"2303.12233","title":"LOKI: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulation","created":"2023-03-21","cited_by_count":36,"area":null},{"id":"2404.15381","title":"Advances and Open Challenges in Federated Foundation Models","created":"2024-04-23","cited_by_count":36,"area":null},{"id":"2203.09747","title":"Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization","created":"2022-03-18","cited_by_count":35,"area":null},{"id":"2109.03775","title":"FedZKT: Zero-Shot Knowledge Transfer towards Resource-Constrained Federated Learning with Heterogeneous On-Device Models","created":"2021-09-08","cited_by_count":31,"area":null},{"id":"2111.14655","title":"FedHM: Efficient Federated Learning for Heterogeneous Models via Low-rank Factorization","created":"2021-11-29","cited_by_count":31,"area":null},{"id":"2201.02932","title":"A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning","created":"2022-01-09","cited_by_count":31,"area":null},{"id":"2308.08643","title":"Towards Personalized Federated Learning via Heterogeneous Model Reassembly","created":"2023-08-16","cited_by_count":31,"area":null},{"id":"2301.02982","title":"Why Batch Normalization Damage Federated Learning on Non-IID Data?","created":"2023-01-08","cited_by_count":29,"area":null},{"id":"2401.04472","title":"A Survey on Efficient Federated Learning Methods for Foundation Model Training","created":"2024-01-09","cited_by_count":29,"area":null},{"id":"2305.14107","title":"Federated Generalized Category Discovery","created":"2023-05-23","cited_by_count":27,"area":null},{"id":"2402.15070","title":"Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting","created":"2024-02-23","cited_by_count":27,"area":null},{"id":"2302.05601","title":"Pruning Deep Neural Networks from a Sparsity Perspective","created":"2023-02-11","cited_by_count":25,"area":null},{"id":"2205.13925","title":"DELTA: Diverse Client Sampling for Fasting Federated Learning","created":"2022-05-27","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":"2402.01350","title":"pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated Learning","created":"2024-02-02","cited_by_count":24,"area":"moe"},{"id":"2112.13246","title":"Towards Federated Learning on Time-Evolving Heterogeneous Data","created":"2021-12-25","cited_by_count":23,"area":null},{"id":"2210.14226","title":"FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural Networks","created":"2022-10-25","cited_by_count":23,"area":null},{"id":"2204.13169","title":"FedShuffle: Recipes for Better Use of Local Work in Federated Learning","created":"2022-04-27","cited_by_count":21,"area":null},{"id":"2307.10655","title":"A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency","created":"2023-07-20","cited_by_count":21,"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":"2409.13503","title":"SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Framework","created":"2024-09-20","cited_by_count":21,"area":null},{"id":"2201.08459","title":"Federated Learning with Heterogeneous Architectures using Graph HyperNetworks","created":"2022-01-20","cited_by_count":19,"area":null},{"id":"2202.00807","title":"Federated Learning Challenges and Opportunities: An Outlook","created":"2022-02-01","cited_by_count":19,"area":null},{"id":"2207.09413","title":"SphereFed: Hyperspherical Federated Learning","created":"2022-07-19","cited_by_count":19,"area":null},{"id":"2210.03277","title":"Rethinking Normalization Methods in Federated Learning","created":"2022-10-07","cited_by_count":19,"area":null},{"id":"2311.11227","title":"FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients","created":"2023-11-19","cited_by_count":19,"area":null},{"id":"2405.03409","title":"LightTR: A Lightweight Framework for Federated Trajectory Recovery","created":"2024-05-06","cited_by_count":19,"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":"2311.13250","title":"FedHCA$^2$: Towards Hetero-Client Federated Multi-Task Learning","created":"2023-11-22","cited_by_count":17,"area":null},{"id":"2403.15760","title":"An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning","created":"2024-03-23","cited_by_count":17,"area":null},{"id":"2504.21099","title":"A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning","created":"2025-04-29","cited_by_count":17,"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":"2308.11841","title":"A Survey for Federated Learning Evaluations: Goals and Measures","created":"2023-08-23","cited_by_count":16,"area":null},{"id":"2503.04543","title":"Keeping Yourself is Important in Downstream Tuning Multimodal Large Language Model","created":"2025-03-06","cited_by_count":16,"area":"lr"},{"id":"2201.11865","title":"FedLite: A Scalable Approach for Federated Learning on Resource-constrained Clients","created":"2022-01-28","cited_by_count":15,"area":null},{"id":"2208.10134","title":"Machine Learning with Confidential Computing: A Systematization of Knowledge","created":"2022-08-22","cited_by_count":15,"area":null},{"id":"2206.05891","title":"Anchor Sampling for Federated Learning with Partial Client Participation","created":"2022-06-13","cited_by_count":14,"area":null},{"id":"2212.08272","title":"Communication-Efficient Federated Learning for Heterogeneous Edge Devices Based on Adaptive Gradient Quantization","created":"2022-12-16","cited_by_count":14,"area":null},{"id":"2302.03662","title":"Federated Learning with Regularized Client Participation","created":"2023-02-07","cited_by_count":14,"area":null},{"id":"2306.17465","title":"FedBone: Towards Large-Scale Federated Multi-Task Learning","created":"2023-06-30","cited_by_count":14,"area":null},{"id":"2402.01096","title":"Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance","created":"2024-02-02","cited_by_count":14,"area":null},{"id":"2406.12844","title":"Synergizing Foundation Models and Federated Learning: A Survey","created":"2024-06-18","cited_by_count":14,"area":null},{"id":"2112.13939","title":"SPIDER: Searching Personalized Neural Architecture for Federated Learning","created":"2021-12-27","cited_by_count":13,"area":null},{"id":"2208.13141","title":"Federated Learning of Large Models at the Edge via Principal Sub-Model Training","created":"2022-08-28","cited_by_count":13,"area":null},{"id":"2303.14453","title":"Federated Learning without Full Labels: A Survey","created":"2023-03-25","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":"2405.03636","title":"The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscape","created":"2024-05-06","cited_by_count":13,"area":null},{"id":"2110.14205","title":"FedPrune: Towards Inclusive Federated Learning","created":"2021-10-27","cited_by_count":12,"area":null},{"id":"2206.10844","title":"Quantization Robust Federated Learning for Efficient Inference on Heterogeneous Devices","created":"2022-06-22","cited_by_count":12,"area":null},{"id":"2208.13092","title":"Lottery Aware Sparsity Hunting: Enabling Federated Learning on Resource-Limited Edge","created":"2022-08-27","cited_by_count":12,"area":null},{"id":"2210.16105","title":"Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout","created":"2022-10-28","cited_by_count":12,"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":"2405.14791","title":"Recurrent Early Exits for Federated Learning with Heterogeneous Clients","created":"2024-05-23","cited_by_count":12,"area":"distill"},{"id":"2202.11453","title":"Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization","created":"2022-02-23","cited_by_count":11,"area":null},{"id":"2204.02321","title":"SAFARI: Sparsity enabled Federated Learning with Limited and Unreliable Communications","created":"2022-04-05","cited_by_count":11,"area":null},{"id":"2209.14849","title":"Federated Stain Normalization for Computational Pathology","created":"2022-09-29","cited_by_count":11,"area":null},{"id":"2210.17281","title":"GNN at the Edge: Cost-Efficient Graph Neural Network Processing over Distributed Edge Servers","created":"2022-10-31","cited_by_count":11,"area":null},{"id":"2112.08761","title":"DISTREAL: Distributed Resource-Aware Learning in Heterogeneous Systems","created":"2021-12-16","cited_by_count":10,"area":null},{"id":"2301.03062","title":"AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices","created":"2023-01-08","cited_by_count":10,"area":null},{"id":"2310.09789","title":"FLrce: Resource-Efficient Federated Learning with Early-Stopping Strategy","created":"2023-10-15","cited_by_count":10,"area":null},{"id":"2401.08977","title":"FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Data","created":"2024-01-17","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":"2106.10196","title":"Federated Robustness Propagation: Sharing Robustness in Heterogeneous Federated Learning","created":"2021-06-18","cited_by_count":9,"area":null},{"id":"2109.03999","title":"Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies","created":"2021-09-09","cited_by_count":9,"area":null},{"id":"2204.03726","title":"Decentralized Event-Triggered Federated Learning with Heterogeneous Communication Thresholds","created":"2022-04-07","cited_by_count":9,"area":null},{"id":"2206.04887","title":"Deep Leakage from Model in Federated Learning","created":"2022-06-10","cited_by_count":9,"area":null},{"id":"2206.10032","title":"Communication-Efficient Federated Learning With Data and Client Heterogeneity","created":"2022-06-20","cited_by_count":9,"area":null}],"next_cursor":"100"}