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 23512400 of 6771 papers

TitleStatusHype
Communication-Efficient Hybrid Federated Learning for E-health with Horizontal and Vertical Data Partitioning0
A Federated Learning-based Lightweight Network with Zero Trust for UAV Authentication0
Accelerating Federated Learning by Selecting Beneficial Herd of Local Gradients0
A State Alignment-Centric Approach to Federated System Identification: The FedAlign Framework0
FedDriveScore: Federated Scoring Driving Behavior with a Mixture of Metric Distributions0
Joint Model Pruning and Resource Allocation for Wireless Time-triggered Federated Learning0
Communication-Efficient Framework for Distributed Image Semantic Wireless Transmission0
FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia0
FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning0
Assortment of Attention Heads: Accelerating Federated PEFT with Head Pruning and Strategic Client Selection0
Federated Crowdsensing: Framework and Challenges0
Federated Deep Equilibrium Learning: Harnessing Compact Global Representations to Enhance Personalization0
Federated Deep Subspace Clustering0
FedDMF: Privacy-Preserving User Attribute Prediction using Deep Matrix Factorization0
FedDKD: Federated Learning with Decentralized Knowledge Distillation0
Communication-Efficient Federated Low-Rank Update Algorithm and its Connection to Implicit Regularization0
Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks0
A Snapshot of the Frontiers of Client Selection in Federated Learning0
FedDP: Privacy-preserving method based on federated learning for histopathology image segmentation0
FedDQ: Communication-Efficient Federated Learning with Descending Quantization0
A Federated Learning-based Industrial Health Prognostics for Heterogeneous Edge Devices using Matched Feature Extraction0
FedDistill: Global Model Distillation for Local Model De-Biasing in Non-IID Federated Learning0
FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation0
FedDRL: A Trustworthy Federated Learning Model Fusion Method Based on Staged Reinforcement Learning0
FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning0
FedDRO: Federated Compositional Optimization for Distributionally Robust Learning0
FedDiscrete: A Secure Federated Learning Algorithm Against Weight Poisoning0
FedDrop: Trajectory-weighted Dropout for Efficient Federated Learning0
FedDR – Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization0
FedDr+: Stabilizing Dot-regression with Global Feature Distillation for Federated Learning0
FedDTG:Federated Data-Free Knowledge Distillation via Three-Player Generative Adversarial Networks0
FedDTPT: Federated Discrete and Transferable Prompt Tuning for Black-Box Large Language Models0
Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selection0
FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server0
Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data0
Federated Cubic Regularized Newton Learning with Sparsification-amplified Differential Privacy0
FedDyMem: Efficient Federated Learning with Dynamic Memory and Memory-Reduce for Unsupervised Image Anomaly Detection0
FedEAT: A Robustness Optimization Framework for Federated LLMs0
Entropy-driven Fair and Effective Federated Learning0
A Simple Data Augmentation for Feature Distribution Skewed Federated Learning0
FedECADO: A Dynamical System Model of Federated Learning0
Fed-EC: Bandwidth-Efficient Clustering-Based Federated Learning For Autonomous Visual Robot Navigation0
FedED: Federated Learning via Ensemble Distillation for Medical Relation Extraction0
FedEdge AI-TC: A Semi-supervised Traffic Classification Method based on Trusted Federated Deep Learning for Mobile Edge Computing0
FedEFC: Federated Learning Using Enhanced Forward Correction Against Noisy Labels0
FedEFM: Federated Endovascular Foundation Model with Unseen Data0
Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading0
FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discrimination0
A SER-based Device Selection Mechanism in Multi-bits Quantization Federated Learning0
Mitigating Data Absence in Federated Learning Using Privacy-Controllable Data Digests0
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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