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 29513000 of 6771 papers

TitleStatusHype
Bayesian Personalized Federated Learning with Shared and Personalized Uncertainty Representations0
Federated Deep Equilibrium Learning: Harnessing Compact Global Representations to Enhance Personalization0
Markov Chain Mirror Descent On Data Federation0
FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware SchedulerCode1
PLMM: Personal Large Language Models on Mobile Devices0
Federated Learning Under Restricted User Availability0
REPA: Client Clustering without Training and Data Labels for Improved Federated Learning in Non-IID Settings0
Tackling Intertwined Data and Device Heterogeneities in Federated Learning with Unlimited StalenessCode0
REWAFL: Residual Energy and Wireless Aware Participant Selection for Efficient Federated Learning over Mobile Devices0
DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning0
FedDrive v2: an Analysis of the Impact of Label Skewness in Federated Semantic Segmentation for Autonomous DrivingCode1
Importance of Smoothness Induced by Optimizers in FL4ASR: Towards Understanding Federated Learning for End-to-End ASR0
Federated Short-Term Load Forecasting with Personalization Layers for Heterogeneous Clients0
Expressive variational quantum circuits provide inherent privacy in federated learning0
Improving Machine Learning Robustness via Adversarial Training0
Lockdown: Backdoor Defense for Federated Learning with Isolated Subspace TrainingCode1
Lockdown: Backdoor Defense for Federated Learning with Isolated Subspace TrainingCode1
Enabling Quartile-based Estimated-Mean Gradient Aggregation As Baseline for Federated Image Classifications0
Efficient Core-selecting Incentive Mechanism for Data Sharing in Federated Learning0
Likelihood-based Sensor Calibration using Affine Transformation0
Federated Learning with Neural Graphical Models0
Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive CollaborationCode1
Preconditioned Federated Learning0
Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems0
Clustered FedStack: Intermediate Global Models with Bayesian Information Criterion0
Privacy Preservation in Artificial Intelligence and Extended Reality (AI-XR) Metaverses: A Survey0
Toward efficient resource utilization at edge nodes in federated learning0
FedWOA: A Federated Learning Model that uses the Whale Optimization Algorithm for Renewable Energy Prediction0
Towards Energy-Aware Federated Traffic Prediction for Cellular NetworksCode1
Communication-Efficient Federated Learning via Regularized Sparse Random Networks0
FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learning0
FedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup for Non-IID Data0
A Multi-Token Coordinate Descent Method for Semi-Decentralized Vertical Federated Learning0
Federated Learning in Temporal Heterogeneity0
User Assignment and Resource Allocation for Hierarchical Federated Learning over Wireless Networks0
UNIDEAL: Curriculum Knowledge Distillation Federated Learning0
XFedHunter: An Explainable Federated Learning Framework for Advanced Persistent Threat Detection in SDN0
FedDCSR: Federated Cross-domain Sequential Recommendation via Disentangled Representation LearningCode1
FedJudge: Federated Legal Large Language ModelCode1
FedFNN: Faster Training Convergence Through Update Predictions in Federated Recommender Systems0
Communication Efficient Private Federated Learning Using DitheringCode0
Client-side Gradient Inversion Against Federated Learning from Poisoning0
Mitigating Adversarial Attacks in Federated Learning with Trusted Execution EnvironmentsCode0
Learning From Drift: Federated Learning on Non-IID Data via Drift Regularization0
Federated PAC-Bayesian Learning on Non-IID data0
FedDIP: Federated Learning with Extreme Dynamic Pruning and Incremental RegularizationCode1
Tackling the Non-IID Issue in Heterogeneous Federated Learning by Gradient Harmonization0
Mitigating Group Bias in Federated Learning for Heterogeneous Devices0
Fingerprint Attack: Client De-Anonymization in Federated LearningCode0
Federated Learning for Large-Scale Scene Modeling with Neural Radiance Fields0
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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