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

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Showing 3251–3300 of 6771 papers

TitleStatusHype
FedGrAINS: Personalized SubGraph Federated Learning with Adaptive Neighbor Sampling—0
FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix—0
FedGraph: an Aggregation Method from Graph Perspective—0
Federated Learning for Emoji Prediction in a Mobile Keyboard—0
FedGraph: Federated Graph Learning with Intelligent Sampling—0
CRS-FL: Conditional Random Sampling for Communication-Efficient and Privacy-Preserving Federated Learning—0
FedGRec: Dynamic Spatio-Temporal Federated Graph Learning for Secure and Efficient Cross-Border Recommendations—0
FedGRec: Federated Graph Recommender System with Lazy Update of Latent Embeddings—0
FedGreen: Carbon-aware Federated Learning with Model Size Adaptation—0
FedGreen: Federated Learning with Fine-Grained Gradient Compression for Green Mobile Edge Computing—0
A Systematic Review of Federated Generative Models—0
FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise—0
A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates—0
Federated Learning for Efficient Condition Monitoring and Anomaly Detection in Industrial Cyber-Physical Systems—0
Federated Learning Forecasting for Strengthening Grid Reliability and Enabling Markets for Resilience—0
FedGTST: Boosting Global Transferability of Federated Models via Statistics Tuning—0
CRSFL: Cluster-based Resource-aware Split Federated Learning for Continuous Authentication—0
FedHAP: Fast Federated Learning for LEO Constellations Using Collaborative HAPs—0
FedHAP: Federated Hashing with Global Prototypes for Cross-silo Retrieval—0
Federated Learning for Early Dropout Prediction on Healthy Ageing Applications—0
FedHB: Hierarchical Bayesian Federated Learning—0
Federated Learning for Drowsiness Detection in Connected Vehicles—0
Cross-Training with Multi-View Knowledge Fusion for Heterogenous Federated Learning—0
A Systematic Literature Review on Federated Learning: From A Model Quality Perspective—0
Federated Learning for distribution skewed data using sample weights—0
Federated Learning for Distributed Spectrum Sensing in NextG Communication Networks—0
Federated Learning for Distributed Energy-Efficient Resource Allocation—0
FedHeN: Federated Learning in Heterogeneous Networks—0
Federated Learning for Discrete Optimal Transport with Large Population under Incomplete Information—0
FedHide: Federated Learning by Hiding in the Neighbors—0
A Systematic Literature Review on Federated Machine Learning: From A Software Engineering Perspective—0
A Generalized Meta Federated Learning Framework with Theoretical Convergence Guarantees—0
FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation—0
FedHM: Efficient Federated Learning for Heterogeneous Models via Low-rank Factorization—0
FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring—0
Adaptive Control of Client Selection and Gradient Compression for Efficient Federated Learning—0
FedHPL: Efficient Heterogeneous Federated Learning with Prompt Tuning and Logit Distillation—0
FedHPO-B: A Benchmark Suite for Federated Hyperparameter Optimization—0
FedHQ: Hybrid Runtime Quantization for Federated Learning—0
FedHyper: A Universal and Robust Learning Rate Scheduler for Federated Learning with Hypergradient Descent—0
Enhancing Convergence in Federated Learning: A Contribution-Aware Asynchronous Approach—0
2CP: Decentralized Protocols to Transparently Evaluate Contributivity in Blockchain Federated Learning Environments—0
Strategic Federated Learning: Application to Smart Meter Data Clustering—0
FedIFL: A federated cross-domain diagnostic framework for motor-driven systems with inconsistent fault modes—0
Federated Learning for Diffusion Models—0
Federated Learning for Diabetic Retinopathy Diagnosis: Enhancing Accuracy and Generalizability in Under-Resourced Regions—0
Cross-Silo Federated Learning for Multi-Tier Networks with Vertical and Horizontal Data Partitioning—0
Federated Learning for Data Market: Shapley-UCB for Seller Selection and Incentives—0
FedIN: Federated Intermediate Layers Learning for Model Heterogeneity—0
Cross-Silo Federated Learning: Challenges and Opportunities—0
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All datasetsCityscapes heterogeneousLandmarks-User-160kCIFAR-100 (alpha=0, 10 clients per round)CIFAR-100 (alpha=0, 20 clients per round)CIFAR-100 (alpha=0.5, 10 clients per round)CIFAR-100 (alpha=0.5, 20 clients per round)CIFAR-100 (alpha=0.5, 5 clients per round)CIFAR-100 (alpha=0, 5 clients per round)CIFAR-100 (alpha=1000, 10 clients per round)CIFAR-100 (alpha=1000, 20 clients per round)CIFAR-100 (alpha=1000, 5 clients per round)CIFAR100 (alpha=0.3, 10 clients per round)

Benchmark Results

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