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 65516600 of 6771 papers

TitleStatusHype
Federated Learning for Localization: A Privacy-Preserving Crowdsourcing Method0
FedDANE: A Federated Newton-Type MethodCode1
Think Locally, Act Globally: Federated Learning with Local and Global RepresentationsCode1
Acceleration for Compressed Gradient Descent in Distributed Optimization0
Instance-hiding Schemes for Private Distributed Learning0
Communication-Efficient Federated Learning with Sketching0
From Local SGD to Local Fixed Point Methods for Federated Learning0
FedBoost: A Communication-Efficient Algorithm for Federated Learning0
Robust Aggregation for Federated LearningCode0
Robust Federated Learning Through Representation Matching and Adaptive Hyper-parameters0
Towards Deep Federated Defenses Against Malware in Cloud Ecosystems0
Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT NetworksCode1
Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer0
Attack-Resistant Federated Learning with Residual-based ReweightingCode0
Distributed Fixed Point Methods with Compressed Iterates0
Asynchronous Federated Learning with Differential Privacy for Edge Intelligence0
Private Federated Learning with Domain Adaptation0
Parallel Restarted SPIDER -- Communication Efficient Distributed Nonconvex Optimization with Optimal Computation Complexity0
HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning0
Representation of Federated Learning via Worst-Case Robust Optimization Theory0
Advances and Open Problems in Federated LearningCode2
Privacy-Preserving Blockchain Based Federated Learning with Differential Data Sharing0
Learn Electronic Health Records by Fully Decentralized Federated Learning0
Distributed Machine Learning with Sparse Heterogeneous Data0
Federated Learning with Personalization Layers0
Preserving Patient Privacy while Training a Predictive Model of In-hospital Mortality0
A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regression0
Federated Learning with Autotuned Communication-Efficient Secure Aggregation0
Free-riders in Federated Learning: Attacks and Defenses0
Local Model Poisoning Attacks to Byzantine-Robust Federated Learning0
Federated Learning for Ranking Browser History Suggestions0
Differentially Private Federated Variational InferenceCode0
Federated Learning with Bayesian Differential Privacy0
Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator0
Optimal Robust Learning of Discrete Distributions from Batches0
Can You Really Backdoor Federated Learning?0
Information-Theoretic Perspective of Federated Learning0
Generative Models for Effective ML on Private, Decentralized DatasetsCode1
Federated and Differentially Private Learning for Electronic Health Records0
Federated Learning for Healthcare Informatics0
Hyper-Sphere Quantization: Communication-Efficient SGD for Federated LearningCode0
Privacy is What We Care About: Experimental Investigation of Federated Learning on Edge Devices0
Practical Federated Gradient Boosting Decision TreesCode1
L-FGADMM: Layer-Wise Federated Group ADMM for Communication Efficient Decentralized Deep Learning0
Revocable Federated Learning: A Benchmark of Federated Forest0
Energy Efficient Federated Learning Over Wireless Communication Networks0
Secure Federated Submodel LearningCode0
Enhancing the Privacy of Federated Learning with Sketching0
Asynchronous Online Federated Learning for Edge Devices with Non-IID Data0
Federated Adversarial Domain Adaptation0
Show:102550
← PrevPage 132 of 136Next →

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