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 43014350 of 6771 papers

TitleStatusHype
FedMinds: Privacy-Preserving Personalized Brain Visual Decoding0
FedMint: Intelligent Bilateral Client Selection in Federated Learning with Newcomer IoT Devices0
Fed-MIWAE: Federated Imputation of Incomplete Data via Deep Generative Models0
FedMKGC: Privacy-Preserving Federated Multilingual Knowledge Graph Completion0
FedMM: Federated Multi-Modal Learning with Modality Heterogeneity in Computational Pathology0
FedMM-X: A Trustworthy and Interpretable Framework for Federated Multi-Modal Learning in Dynamic Environments0
FedMobile: Enabling Knowledge Contribution-aware Multi-modal Federated Learning with Incomplete Modalities0
FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregation0
FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts0
FedMorph: Communication Efficient Federated Learning via Morphing Neural Network0
FedMPQ: Secure and Communication-Efficient Federated Learning with Multi-codebook Product Quantization0
Is Aggregation the Only Choice? Federated Learning via Layer-wise Model Recombination0
FedMR: Fedreated Learning via Model Recombination0
FedMS: Federated Learning with Mixture of Sparsely Activated Foundations Models0
FedMSGL: A Self-Expressive Hypergraph Based Federated Multi-View Learning0
FedMT: Federated Learning with Mixed-type Labels0
FedNano: Toward Lightweight Federated Tuning for Pretrained Multimodal Large Language Models0
FedNAS: Federated Deep Learning via Neural Architecture Search0
FedNC: A Secure and Efficient Federated Learning Method with Network Coding0
Fed-NDIF: A Noise-Embedded Federated Diffusion Model For Low-Count Whole-Body PET Denoising0
FedNER: Privacy-preserving Medical Named Entity Recognition with Federated Learning0
FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction0
FedNet2Net: Saving Communication and Computations in Federated Learning with Model Growing0
FedNIA: Noise-Induced Activation Analysis for Mitigating Data Poisoning in FL0
FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction0
Fed-NILM: A Federated Learning-based Non-Intrusive Load Monitoring Method for Privacy-Protection0
FedNILM: Applying Federated Learning to NILM Applications at the Edge0
FedNL: Making Newton-Type Methods Applicable to Federated Learning0
FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks0
FedNNNN: Norm-Normalized Neural Network Aggregation for Fast and Accurate Federated Learning0
FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels0
FedNorm: Modality-Based Normalization in Federated Learning for Multi-Modal Liver Segmentation0
FedNS: Improving Federated Learning for collaborative image classification on mobile clients0
FedNST: Federated Noisy Student Training for Automatic Speech Recognition0
FedORGP: Guiding Heterogeneous Federated Learning with Orthogonality Regularization on Global Prototypes0
FedOCR: Communication-Efficient Federated Learning for Scene Text Recognition0
FedOpenHAR: Federated Multi-Task Transfer Learning for Sensor-Based Human Activity Recognition0
FedORA: Resource Allocation for Federated Learning in ORAN using Radio Intelligent Controllers0
FedorAS: Federated Architecture Search under system heterogeneity0
FedOSAA: Improving Federated Learning with One-Step Anderson Acceleration0
FedP^2EFT: Federated Learning to Personalize Parameter Efficient Fine-Tuning for Multilingual LLMs0
FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity0
FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and Model-Heterogeneous Federated Learning0
FedPAGE: A Fast Local Stochastic Gradient Method for Communication-Efficient Federated Learning0
FedPaI: Achieving Extreme Sparsity in Federated Learning via Pruning at Initialization0
FedPandemic: A Cross-Device Federated Learning Approach Towards Elementary Prognosis of Diseases During a Pandemic0
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization0
FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computing0
FedParsing: a Semi-Supervised Federated Learning Model on Semantic Parsing0
FedPartWhole: Federated domain generalization via consistent part-whole hierarchies0
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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