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 4801–4850 of 6771 papers

TitleStatusHype
Hybrid quantum image classification and federated learning for hepatic steatosis diagnosis—0
HYDRA-FL: Hybrid Knowledge Distillation for Robust and Accurate Federated Learning—0
WW-FL: Secure and Private Large-Scale Federated Learning—0
Hyperdimensional Computing Empowered Federated Foundation Model over Wireless Networks for Metaverse—0
HyperFed: Hyperbolic Prototypes Exploration with Consistent Aggregation for Non-IID Data in Federated Learning—0
Hyper-parameter Optimization for Federated Learning with Step-wise Adaptive Mechanism—0
Hyperparameter Optimization for SecureBoost via Constrained Multi-Objective Federated Learning—0
Hyperparameter Optimization through Neural Network Partitioning—0
HyperTune: Dynamic Hyperparameter Tuning For Efficient Distribution of DNN Training Over Heterogeneous Systems—0
ICAFS: Inter-Client-Aware Feature Selection for Vertical Federated Learning—0
IMFL-AIGC: Incentive Mechanism Design for Federated Learning Empowered by Artificial Intelligence Generated Content—0
Immersion and Invariance-based Coding for Privacy-Preserving Federated Learning—0
Impact of network topology on the performance of Decentralized Federated Learning—0
Implicit Gradient Alignment in Distributed and Federated Learning—0
Importance of Smoothness Induced by Optimizers in FL4ASR: Towards Understanding Federated Learning for End-to-End ASR—0
(Im)possibility of Collective Intelligence—0
Improved Convergence Analysis and SNR Control Strategies for Federated Learning in the Presence of Noise—0
Faster Non-Convex Federated Learning via Global and Local Momentum—0
Improved Generalization Bounds for Communication Efficient Federated Learning—0
Improved Information Theoretic Generalization Bounds for Distributed and Federated Learning—0
Improving Accelerated Federated Learning with Compression and Importance Sampling—0
Improving accuracy and convergence of federated learning edge computing methods for generalized DER forecasting applications in power grid—0
Improving Accuracy of Federated Learning in Non-IID Settings—0
Improving Fairness for Data Valuation in Horizontal Federated Learning—0
Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters—0
Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories—0
Improving Federated Relational Data Modeling via Basis Alignment and Weight Penalty—0
Improving Group Connectivity for Generalization of Federated Deep Learning—0
Improving LoRA in Privacy-preserving Federated Learning—0
Improving Machine Learning Robustness via Adversarial Training—0
Improving Multi-Center Generalizability of GAN-Based Fat Suppression using Federated Learning—0
Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training—0
Improving on-device speaker verification using federated learning with privacy—0
Balancing Privacy and Performance for Private Federated Learning Algorithms—0
Improving the Classification Effect of Clinical Images of Diseases for Multi-Source Privacy Protection—0
Improving the Model Consistency of Decentralized Federated Learning—0
Improving the Robustness of Federated Learning for Severely Imbalanced Datasets—0
Incentive Allocation in Vertical Federated Learning Based on Bankruptcy Problem—0
Incentive-Compatible Federated Learning with Stackelberg Game Modeling—0
Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach—0
Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learning—0
Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participation—0
Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective—0
Incentives for Federated Learning: a Hypothesis Elicitation Approach—0
Incentivize Contribution and Learn Parameters Too: Federated Learning with Strategic Data Owners—0
Incentivizing Federated Learning—0
Incentivizing High-quality Participation From Federated Learning Agents—0
Incentivizing Honesty among Competitors in Collaborative Learning and Optimization—0
Incentivizing Inclusive Contributions in Model Sharing Markets—0
Incentivizing Multi-Tenant Split Federated Learning for Foundation Models at the Network Edge—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