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 6001–6050 of 6771 papers

TitleStatusHype
Personalized Federated Deep Learning for Pain Estimation From Face ImagesCode0
FedAR: Activity and Resource-Aware Federated Learning Model for Distributed Mobile Robots—0
On the Practicality of Differential Privacy in Federated Learning by Tuning Iteration Times—0
Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks—0
Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial SystemsCode1
Robust Blockchained Federated Learning with Model Validation and Proof-of-Stake Inspired ConsensusCode1
Federated Intelligence for Active Queue Management in Inter-Domain CongestionCode0
DiPSeN: Differentially Private Self-normalizing Neural Networks For Adversarial Robustness in Federated Learning—0
Differentially Private Federated Learning for Cancer PredictionCode0
Architectural Patterns for the Design of Federated Learning Systems—0
Federated Learning over Noisy Channels: Convergence Analysis and Design Examples—0
IPLS : A Framework for Decentralized Federated LearningCode0
Federated Learning-Based Risk-Aware Decision toMitigate Fake Task Impacts on CrowdsensingPlatforms—0
Fusion of Federated Learning and Industrial Internet of Things: A Survey—0
Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation—0
Ensemble Attention Distillation for Privacy-Preserving Federated Learning—0
Federated Learning for Non-IID Data via Unified Feature Learning and Optimization Objective Alignment—0
Fidel: Reconstructing Private Training Samples from Weight Updates in Federated LearningCode0
Dynamic Federated Learning-Based Economic Framework for Internet-of-Vehicles—0
Robust Federated Learning for Neural Networks—0
D2p-fed:Differentially Private Federated Learning with Efficient Communication—0
Delay-Tolerant Local SGD for Efficient Distributed Training—0
Practical Locally Private Federated Learning with Communication Efficiency—0
FedMes: Speeding Up Federated Learning with Multiple Edge Servers—0
CAFE: Catastrophic Data Leakage in Federated Learning—0
Few-Round Learning for Federated Learning—0
F^2ed-Learning: Good Fences Make Good Neighbors—0
Federated learning using mixture of experts—0
Federated Learning with Decoupled Probabilistic-Weighted Gradient Aggregation—0
Sself: Robust Federated Learning against Stragglers and Adversaries—0
A Stochastic Gradient Langevin Dynamics Algorithm For Noise Intrinsic Federated Learning—0
Federated Averaging as Expectation Maximization—0
Secure Federated Learning of User Verification Models—0
End-to-End on-device Federated Learning: A case study—0
BAFFLE: TOWARDS RESOLVING FEDERATED LEARNING’S DILEMMA - THWARTING BACKDOOR AND INFERENCE ATTACKS—0
Bayesian Federated Learning over Wireless Networks—0
PFL-MoE: Personalized Federated Learning Based on Mixture of ExpertsCode1
Timely Communication in Federated Learning—0
Federated Multi-Agent Actor-Critic Learning for Age Sensitive Mobile Edge Computing—0
Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity—0
Federated Unlearning—0
FLTrust: Byzantine-robust Federated Learning via Trust BootstrappingCode1
Decentralized Federated Learning via Mutual Knowledge Transfer—0
Comparison of Privacy-Preserving Distributed Deep Learning Methods in Healthcare—0
To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices—0
Hybrid Federated Learning: Algorithms and ImplementationCode0
Turn Signal Prediction: A Federated Learning Case Study—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
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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