SOTAVerified

Federated Learning

Federated Learning is a machine learning approach that allows multiple devices or entities to collaboratively train a shared model without exchanging their data with each other. Instead of sending data to a central server for training, the model is trained locally on each device, and only the model updates are sent to the central server, where they are aggregated to improve the shared model.

This approach allows for privacy-preserving machine learning, as each device keeps its data locally and only shares the information needed to improve the model.

Papers

Showing 14511500 of 6771 papers

TitleStatusHype
FedModule: A Modular Federated Learning FrameworkCode2
Enhancing Quantum Security over Federated Learning via Post-Quantum Cryptography0
Heterogeneity-Aware Cooperative Federated Edge Learning with Adaptive Computation and Communication Compression0
Active-Passive Federated Learning for Vertically Partitioned Multi-view Data0
Can We Theoretically Quantify the Impacts of Local Updates on the Generalization Performance of Federated Learning?0
VFLGAN-TS: Vertical Federated Learning-based Generative Adversarial Networks for Publication of Vertically Partitioned Time-Series Data0
FedPCL-CDR: A Federated Prototype-based Contrastive Learning Framework for Privacy-Preserving Cross-domain RecommendationCode0
Wind turbine condition monitoring based on intra- and inter-farm federated learningCode0
Robust Federated Finetuning of Foundation Models via Alternating Minimization of LoRA0
CoAst: Validation-Free Contribution Assessment for Federated Learning based on Cross-Round Valuation0
FedMinds: Privacy-Preserving Personalized Brain Visual Decoding0
Buffer-based Gradient Projection for Continual Federated LearningCode0
Federated Prediction-Powered Inference from Decentralized DataCode0
Collaboratively Learning Federated Models from Noisy Decentralized Data0
Personalized Federated Learning via Active Sampling0
Enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare ApplicationsCode2
GAS: Generative Activation-Aided Asynchronous Split Federated LearningCode0
Achieving Byzantine-Resilient Federated Learning via Layer-Adaptive Sparsified Model AggregationCode0
Fed-MUnet: Multi-modal Federated Unet for Brain Tumor SegmentationCode1
MRI-based and metabolomics-based age scores act synergetically for mortality prediction shown by multi-cohort federated learningCode0
Enabling Trustworthy Federated Learning in Industrial IoT: Bridging the Gap Between Interpretability and Robustness0
DAMe: Personalized Federated Social Event Detection with Dual Aggregation MechanismCode0
Demo: FedCampus: A Real-world Privacy-preserving Mobile Application for Smart Campus via Federated Learning & AnalyticsCode0
FissionVAE: Federated Non-IID Image Generation with Latent Space and Decoder DecompositionCode0
Towards Hyper-parameter-free Federated LearningCode0
Democratizing AI in Africa: FL for Low-Resource Edge Devices0
A Survey for Large Language Models in Biomedicine0
FedMCP: Parameter-Efficient Federated Learning with Model-Contrastive Personalization0
VFLIP: A Backdoor Defense for Vertical Federated Learning via Identification and PurificationCode1
Exploring Selective Layer Fine-Tuning in Federated LearningCode0
Convergent Differential Privacy Analysis for General Federated Learning: the f-DP Perspective0
ModalityMirror: Improving Audio Classification in Modality Heterogeneity Federated Learning with Multimodal Distillation0
Bandwidth-Aware and Overlap-Weighted Compression for Communication-Efficient Federated Learning0
Resource Efficient Asynchronous Federated Learning for Digital Twin Empowered IoT Network0
Federated User Preference Modeling for Privacy-Preserving Cross-Domain RecommendationCode0
Celtibero: Robust Layered Aggregation for Federated Learning0
Hyperdimensional Computing Empowered Federated Foundation Model over Wireless Networks for Metaverse0
Decentralized Federated Learning with Model Caching on Mobile Agents0
Neighborhood and Global Perturbations Supported SAM in Federated Learning: From Local Tweaks To Global Awareness0
FedGlu: A personalized federated learning-based glucose forecasting algorithm for improved performance in glycemic excursion regions0
SAB:A Stealing and Robust Backdoor Attack based on Steganographic Algorithm against Federated Learning0
Towards Case-based Interpretability for Medical Federated Learning0
Submodular Maximization Approaches for Equitable Client Selection in Federated Learning0
Enhancing Vehicle Environmental Awareness via Federated Learning and Automatic Labeling0
A Web-Based Solution for Federated Learning with LLM-Based Automation0
Improving the Classification Effect of Clinical Images of Diseases for Multi-Source Privacy Protection0
FedIMPUTE: Privacy-Preserving Missing Value Imputation for Multi-Site Heterogeneous Electronic Health RecordsCode0
Tackling Data Heterogeneity in Federated Learning via Loss DecompositionCode1
Understanding Data Reconstruction Leakage in Federated Learning from a Theoretical Perspective0
Weight Scope Alignment: A Frustratingly Easy Method for Model Merging0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SiloBN + ASAMmIoU49.75Unverified
2SiloBN + SAMmIoU49.1Unverified
3SiloBNmIoU45.96Unverified
4FedSAM + SWAmIoU43.42Unverified
5FedASAM + SWAmIoU43.02Unverified
6FedAvg + SWAmIoU42.48Unverified
7FedASAMmIoU42.27Unverified
8FedSAMmIoU41.22Unverified
9FedAvgmIoU38.65Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAAcc@1-1262Clients68.32Unverified
2FedSAM + SWAAcc@1-1262Clients68.12Unverified
3FedAvg + SWAAcc@1-1262Clients67.52Unverified
4FedASAMAcc@1-1262Clients64.23Unverified
5FedSAMAcc@1-1262Clients63.72Unverified
6FedAvgAcc@1-1262Clients61.91Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.64Unverified
2FedASAMACC@1-100Clients39.76Unverified
3FedSAM + SWAACC@1-100Clients39.51Unverified
4FedSAMACC@1-100Clients36.93Unverified
5FedAvgACC@1-100Clients36.74Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients41.62Unverified
2FedASAMACC@1-100Clients40.81Unverified
3FedSAM + SWAACC@1-100Clients39.24Unverified
4FedAvgACC@1-100Clients38.59Unverified
5FedSAMACC@1-100Clients38.56Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.72Unverified
2FedSAM + SWAACC@1-100Clients46.76Unverified
3FedASAMACC@1-100Clients46.58Unverified
4FedSAMACC@1-100Clients44.84Unverified
5FedAvgACC@1-100Clients41.27Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.27Unverified
2FedASAMACC@1-100Clients47.78Unverified
3FedSAM + SWAACC@1-100Clients46.47Unverified
4FedSAMACC@1-100Clients46.05Unverified
5FedAvgACC@1-100Clients42.17Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients49.17Unverified
2FedSAM + SWAACC@1-100Clients47.96Unverified
3FedASAMACC@1-100Clients45.61Unverified
4FedSAMACC@1-100Clients44.73Unverified
5FedAvgACC@1-100Clients40.43Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.01Unverified
2FedSAM + SWAACC@1-100Clients39.3Unverified
3FedASAMACC@1-100Clients36.04Unverified
4FedSAMACC@1-100Clients31.04Unverified
5FedAvgACC@1-100Clients30.25Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.97Unverified
2FedASAM + SWAACC@1-100Clients54.79Unverified
3FedSAM + SWAACC@1-100Clients53.67Unverified
4FedSAMACC@1-100Clients53.39Unverified
5FedAvgACC@1-100Clients50.25Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.5Unverified
2FedSAM + SWAACC@1-100Clients54.36Unverified
3FedASAM + SWAACC@1-100Clients54.1Unverified
4FedSAMACC@1-100Clients53.97Unverified
5FedAvgACC@1-100Clients50.66Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.81Unverified
2FedSAMACC@1-100Clients54.01Unverified
3FedSAM + SWAACC@1-100Clients53.9Unverified
4FedASAM + SWAACC@1-100Clients53.86Unverified
5FedAvgACC@1-100Clients49.92Unverified
#ModelMetricClaimedVerifiedStatus
1AdaBestAverage Top-1 Accuracy56.2Unverified