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
Personalized Federated Learning: A Meta-Learning Approach0
Distributed Optimization over Block-Cyclic Data0
Distributed Non-Convex Optimization with Sublinear Speedup under Intermittent Client AvailabilityCode0
ResiliNet: Failure-Resilient Inference in Distributed Neural Networks0
Federated Matrix Factorization: Algorithm Design and Application to Data Clustering0
Robustness analytics to data heterogeneity in edge computingCode0
Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning0
Federated Learning of a Mixture of Global and Local Models0
MDLdroid: a ChainSGD-reduce Approach to Mobile Deep Learning for Personal Mobile Sensing0
Faster On-Device Training Using New Federated Momentum Algorithm0
Knowledge Federation: A Unified and Hierarchical Privacy-Preserving AI Framework0
Prophet: Proactive Candidate-Selection for Federated Learning by Predicting the Qualities of Training and Reporting Phases0
Learning to Detect Malicious Clients for Robust Federated Learning0
Multi-Participant Multi-Class Vertical Federated Learning0
FOCUS: Dealing with Label Quality Disparity in Federated Learning0
Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge0
TiFL: A Tier-based Federated Learning System0
Communication Efficient Federated Learning over Multiple Access Channels0
RPN: A Residual Pooling Network for Efficient Federated Learning0
Intermittent Pulling with Local Compensation for Communication-Efficient Federated Learning0
Overcoming Noisy and Irrelevant Data in Federated Learning0
Convergence Time Optimization for Federated Learning over Wireless Networks0
Stratified cross-validation for unbiased and privacy-preserving federated learning0
A Federated Deep Learning Framework for Privacy Preservation and Communication Efficiency0
Combining Federated and Active Learning for Communication-efficient Distributed Failure Prediction in Aeronautics0
FedVision: An Online Visual Object Detection Platform Powered by Federated LearningCode0
Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach0
Exploiting Unlabeled Data in Smart Cities using Federated Learning0
Federated Learning for Localization: A Privacy-Preserving Crowdsourcing Method0
Instance-hiding Schemes for Private Distributed Learning0
From Local SGD to Local Fixed Point Methods for Federated Learning0
FedBoost: A Communication-Efficient Algorithm for Federated Learning0
Acceleration for Compressed Gradient Descent in Distributed Optimization0
Communication-Efficient Federated Learning with Sketching0
Robust Aggregation for Federated LearningCode0
Robust Federated Learning Through Representation Matching and Adaptive Hyper-parameters0
Towards Deep Federated Defenses Against Malware in Cloud Ecosystems0
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
HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning0
Parallel Restarted SPIDER -- Communication Efficient Distributed Nonconvex Optimization with Optimal Computation Complexity0
Representation of Federated Learning via Worst-Case Robust Optimization Theory0
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
A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regression0
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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