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 64516500 of 6771 papers

TitleStatusHype
Local Differential Privacy based Federated Learning for Internet of Things0
Towards Non-I.I.D. and Invisible Data with FedNAS: Federated Deep Learning via Neural Architecture SearchCode1
Asymmetrical Vertical Federated Learning0
Communication Efficient Federated Learning with Energy Awareness over Wireless Networks0
Secure Federated Learning in 5G Mobile Networks0
Lightwave Power Transfer for Federated Learning-based Wireless NetworksCode1
Decentralized Differentially Private Segmentation with PATE0
Towards Federated Learning With Byzantine-Robust Client WeightingCode0
Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective0
Federated Multi-view Matrix Factorization for Personalized Recommendations0
Resource Management for Blockchain-enabled Federated Learning: A Deep Reinforcement Learning Approach0
FedMAX: Mitigating Activation Divergence for Accurate and Communication-Efficient Federated LearningCode1
Evaluating the Communication Efficiency in Federated Learning Algorithms0
From Local SGD to Local Fixed-Point Methods for Federated Learning0
A Blockchain-based Decentralized Federated Learning Framework with Committee ConsensusCode1
An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies0
Inverting Gradients -- How easy is it to break privacy in federated learning?Code1
Concentrated Differentially Private and Utility Preserving Federated Learning0
End-to-End Evaluation of Federated Learning and Split Learning for Internet of ThingsCode1
Adaptive Personalized Federated LearningCode2
Differentially Private Federated Learning for Resource-Constrained Internet of Things0
Federated Residual Learning0
Semi-Federated LearningCode0
FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection0
Learn to Forget: Machine Unlearning via Neuron Masking0
Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning0
Privacy-Preserving News Recommendation Model LearningCode1
FedNER: Privacy-preserving Medical Named Entity Recognition with Federated Learning0
Federated Learning for Task and Resource Allocation in Wireless High Altitude Balloon Networks0
Privacy-preserving Traffic Flow Prediction: A Federated Learning ApproachCode1
Survey of Personalization Techniques for Federated Learning0
A Compressive Sensing Approach for Federated Learning over Massive MIMO Communication Systems0
Federated Visual Classification with Real-World Data DistributionCode0
The Future of Digital Health with Federated Learning0
Policy-Based Federated LearningCode0
Communication-Efficient Massive UAV Online Path Control: Federated Learning Meets Mean-Field Game Theory0
FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing0
Ternary Compression for Communication-Efficient Federated LearningCode1
Federated Continual Learning with Weighted Inter-client TransferCode1
Practical Privacy Preserving POI Recommendation0
Real-time Federated Evolutionary Neural Architecture Search0
Gradient Statistics Aware Power Control for Over-the-Air Federated Learning0
Threats to Federated Learning: A Survey0
Evaluation Framework For Large-scale Federated LearningCode1
Buffered Asynchronous SGD for Byzantine Learning0
User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization0
Adaptive Federated OptimizationCode1
Federated Over-Air Subspace Tracking from Incomplete and Corrupted DataCode0
An On-Device Federated Learning Approach for Cooperative Model Update between Edge Devices0
LASG: Lazily Aggregated Stochastic Gradients for Communication-Efficient Distributed LearningCode0
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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