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

Showing 29012950 of 6771 papers

TitleStatusHype
Federated Learning with Bayesian Differential Privacy0
FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering0
Federated Learning with Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach0
Federated Learning with Buffered Asynchronous Aggregation0
Federated Learning with Classifier Shift for Class Imbalance0
Federated learning with class imbalance reduction0
Decentralized Gossip Mutual Learning (GML) for brain tumor segmentation on multi-parametric MRI0
Federated Learning with Communication Delay in Edge Networks0
Federated Learning with Compression: Unified Analysis and Sharp Guarantees0
FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling0
A Robust Federated Learning Framework for Undependable Devices at Scale0
Federated Learning with Data-Agnostic Distribution Fusion0
Federated Learning with Decoupled Probabilistic-Weighted Gradient Aggregation0
Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers and Gradient Clipping0
Federated Learning with Differential Privacy0
Federated Learning with Differential Privacy: An Utility-Enhanced Approach0
FedCliP: Federated Learning with Client Pruning0
Federated Learning with Discriminative Naive Bayes Classifier0
Federated learning with distributed fixed design quantum chips and quantum channels0
Federated Learning for Personalized Humor Recognition0
Federated Learning with Domain Generalization0
Federated Learning with Domain Shift Eraser0
Federated Learning with Downlink Device Selection0
Federated Learning with Dual Attention for Robust Modulation Classification under Attacks0
Federated Learning with Dynamic Client Arrival and Departure: Convergence and Rapid Adaptation via Initial Model Construction0
Federated Learning with Dynamic Transformer for Text to Speech0
Federated Multi-Armed Bandits Under Byzantine Attacks0
Federated Learning with Erroneous Communication Links0
Federated Multilinear Principal Component Analysis with Applications in Prognostics0
FedCL-Ensemble Learning: A Framework of Federated Continual Learning with Ensemble Transfer Learning Enhanced for Alzheimer's MRI Classifications while Preserving Privacy0
Federated Learning with Fair Worker Selection: A Multi-Round Submodular Maximization Approach0
Federated Learning with Flexible Architectures0
Federated Learning with Flexible Control0
Federated Learning with GAN-based Data Synthesis for Non-IID Clients0
FedCLEAN: byzantine defense by CLustering Errors of Activation maps in Non-IID federated learning environments0
Federated Learning with Heterogeneous Architectures using Graph HyperNetworks0
Communication Efficient, Differentially Private Distributed Optimization using Correlation-Aware Sketching0
Identifying Backdoor Attacks in Federated Learning via Anomaly Detection0
FedCiR: Client-Invariant Representation Learning for Federated Non-IID Features0
Federated learning with hierarchical clustering of local updates to improve training on non-IID data0
Federated Learning with Hyperparameter-based Clustering for Electrical Load Forecasting0
Decoding FL Defenses: Systemization, Pitfalls, and Remedies0
Communication-Efficient Device Scheduling for Federated Learning Using Lyapunov Optimization0
Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization0
Federated Learning with Instance-Dependent Noisy Label0
Federated Learning with Integrated Sensing, Communication, and Computation: Frameworks and Performance Analysis0
Decoupled Vertical Federated Learning for Practical Training on Vertically Partitioned Data0
FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learning0
Heterogeneity-Aware Client Sampling: A Unified Solution for Consistent Federated Learning0
Federated Multilingual Models for Medical Transcript Analysis0
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Benchmark Results

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