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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6151–6200 of 6771 papers

TitleStatusHype
Secure Federated Learning of User Verification Models—0
Federated Averaging as Expectation Maximization—0
D2p-fed:Differentially Private Federated Learning with Efficient Communication—0
A Stochastic Gradient Langevin Dynamics Algorithm For Noise Intrinsic Federated Learning—0
Federated Learning with Decoupled Probabilistic-Weighted Gradient Aggregation—0
Dynamic Federated Learning-Based Economic Framework for Internet-of-Vehicles—0
F^2ed-Learning: Good Fences Make Good Neighbors—0
CAFE: Catastrophic Data Leakage in Federated Learning—0
Federated Learning for Non-IID Data via Unified Feature Learning and Optimization Objective Alignment—0
FedMes: Speeding Up Federated Learning with Multiple Edge Servers—0
BAFFLE: TOWARDS RESOLVING FEDERATED LEARNING’S DILEMMA - THWARTING BACKDOOR AND INFERENCE ATTACKS—0
Ensemble Attention Distillation for Privacy-Preserving Federated Learning—0
Robust Federated Learning for Neural Networks—0
Delay-Tolerant Local SGD for Efficient Distributed Training—0
Practical Locally Private Federated Learning with Communication Efficiency—0
Bayesian Federated Learning over Wireless Networks—0
Timely Communication in Federated Learning—0
Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity—0
Federated Multi-Agent Actor-Critic Learning for Age Sensitive Mobile Edge Computing—0
Federated Unlearning—0
Decentralized Federated Learning via Mutual Knowledge Transfer—0
Comparison of Privacy-Preserving Distributed Deep Learning Methods in Healthcare—0
Hybrid Federated Learning: Algorithms and ImplementationCode0
Turn Signal Prediction: A Federated Learning Case Study—0
To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices—0
Energy Efficient Federated Learning over Heterogeneous Mobile Devices via Joint Design of Weight Quantization and Wireless Transmission—0
Toward Understanding the Influence of Individual Clients in Federated Learning—0
Fairness and Accuracy in Federated Learning—0
FedADC: Accelerated Federated Learning with Drift Control—0
More Industry-friendly: Federated Learning with High Efficient Design—0
FedeRank: User Controlled Feedback with Federated Recommender Systems—0
Towards open and expandable cognitive AI architectures for large-scale multi-agent human-robot collaborative learning—0
CosSGD: Communication-Efficient Federated Learning with a Simple Cosine-Based Quantization—0
Cost-Effective Federated Learning Design—0
Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning—0
Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters—0
FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring—0
Federated Learning under Importance Sampling—0
Privacy-preserving Decentralized Aggregation for Federated Learning—0
Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directions—0
Adaptive Histogram-Based Gradient Boosted Trees for Federated Learning—0
Privacy-preserving medical image analysis—0
Communication-Computation Efficient Secure Aggregation for Federated Learning—0
Analysis and Optimal Edge Assignment For Hierarchical Federated Learning on Non-IID Data—0
Accurate and Fast Federated Learning via IID and Communication-Aware Grouping—0
Privacy Amplification by DecentralizationCode0
Federated Learning in Unreliable and Resource-Constrained Cellular Wireless NetworksCode0
GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs—0
Poisoning Semi-supervised Federated Learning via Unlabeled Data: Attacks and Defenses—0
Privacy and Robustness in Federated Learning: Attacks and Defenses—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