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 1101–1150 of 6771 papers

TitleStatusHype
Continual Learning for Smart City: A Survey—0
Contrastive Re-localization and History Distillation in Federated CMR Segmentation—0
Blockchain-based Secure Client Selection in Federated Learning—0
Blockchain-based Optimized Client Selection and Privacy Preserved Framework for Federated Learning—0
Anarchic Federated Learning—0
Blockchain-based Monitoring for Poison Attack Detection in Decentralized Federated Learning—0
Blockchain-based Framework for Scalable and Incentivized Federated Learning—0
An Analysis of Untargeted Poisoning Attack and Defense Methods for Federated Online Learning to Rank Systems—0
Blockchain-based Federated Learning with Secure Aggregation in Trusted Execution Environment for Internet-of-Things—0
Blockchain-Based Federated Learning in Mobile Edge Networks with Application in Internet of Vehicles—0
Analyzing the vulnerabilities in SplitFed Learning: Assessing the robustness against Data Poisoning Attacks—0
Blockchain-based Federated Learning for Decentralized Energy Management Systems—0
Blockchain-based Federated Learning for Failure Detection in Industrial IoT—0
Analyzing the Robustness of Decentralized Horizontal and Vertical Federated Learning Architectures in a Non-IID Scenario—0
A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks—0
Achieving Linear Speedup in Asynchronous Federated Learning with Heterogeneous Clients—0
A Systematic Survey of Blockchained Federated Learning—0
Blockchain-Based Federated Learning: Incentivizing Data Sharing and Penalizing Dishonest Behavior—0
Resilience in Online Federated Learning: Mitigating Model-Poisoning Attacks via Partial Sharing—0
Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation—0
Blockchain Assisted Decentralized Federated Learning (BLADE-FL) with Lazy Clients—0
AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning—0
Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensing—0
Label Inference Attacks against Node-level Vertical Federated GNNs—0
Analytic Personalized Federated Meta-Learning—0
Fabricated Flips: Poisoning Federated Learning without Data—0
BlindFL: Vertical Federated Machine Learning without Peeking into Your Data—0
Adapt to Adaptation: Learning to Personalize for Cross-Silo Federated Learning—0
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients—0
A Blockchain-empowered Multi-Aggregator Federated Learning Architecture in Edge Computing with Deep Reinforcement Learning Optimization—0
Blind Federated Learning without initial model—0
Blind Federated Learning via Over-the-Air q-QAM—0
Analysis of Total Variation Minimization for Clustered Federated Learning—0
Analysis of regularized federated learning—0
FSSC: Federated Learning of Transformer Neural Networks for Semantic Image Communication—0
Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization—0
Bit-aware Randomized Response for Local Differential Privacy in Federated Learning—0
AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks—0
Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression—0
Binary Federated Learning with Client-Level Differential Privacy—0
Achieving Fairness in Dermatological Disease Diagnosis through Automatic Weight Adjusting Federated Learning and Personalization—0
A_Blockchain-Based_Decentralized_Federated_Learning_Framework_with_Committee_Consensus—0
Continual Deep Reinforcement Learning for Decentralized Satellite Routing—0
BICompFL: Stochastic Federated Learning with Bi-Directional Compression—0
Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning—0
Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity—0
Bias-Eliminating Augmentation Learning for Debiased Federated Learning—0
Analysis and Optimal Edge Assignment For Hierarchical Federated Learning on Non-IID Data—0
Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoT Networks—0
Biased Over-the-Air Federated Learning under Wireless Heterogeneity—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