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 35013550 of 6771 papers

TitleStatusHype
Efficient Personalized Federated Learning via Sparse Model-AdaptationCode1
Can Fair Federated Learning reduce the need for Personalisation?0
FedCBO: Reaching Group Consensus in Clustered Federated Learning through Consensus-based OptimizationCode1
Incentivising the federation: gradient-based metrics for data selection and valuation in private decentralised training0
Federated Ensemble-Directed Offline Reinforcement LearningCode1
LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning0
A Data-Driven Defense against Edge-case Model Poisoning Attacks on Federated Learning0
Efficient Federated Learning with Enhanced Privacy via Lottery Ticket Pruning in Edge Computing0
FedAVO: Improving Communication Efficiency in Federated Learning with African Vultures OptimizerCode0
Federated Neural Radiance FieldsCode0
DABS: Data-Agnostic Backdoor attack at the Server in Federated Learning0
DepthFL: Depthwise Federated Learning for Heterogeneous Clients0
Personalized Federated Learning under Mixture of DistributionsCode1
Scalable Data Point Valuation in Decentralized LearningCode0
FCA: Taming Long-tailed Federated Medical Image Classification by Classifier AnchoringCode0
Towards the Flatter Landscape and Better Generalization in Federated Learning under Client-level Differential PrivacyCode1
Green Federated Learning Over Cloud-RAN with Limited Fronthual Capacity and Quantized Neural Networks0
Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learning0
FedGrad: Mitigating Backdoor Attacks in Federated Learning Through Local Ultimate Gradients InspectionCode0
Reveal Your Images: Gradient Leakage Attack against Unbiased Sampling-Based Secure AggregationCode0
Client Recruitment for Federated Learning in ICU Length of Stay PredictionCode0
Quality-Adaptive Split-Federated Learning for Segmenting Medical Images with Inaccurate Annotations0
Hyperparameter Optimization through Neural Network Partitioning0
Hierarchical and Decentralised Federated Learning0
Maximizing Model Generalization for Machine Condition Monitoring with Self-Supervised Learning and Federated Learning0
Federated Learning with Uncertainty-Based Client Clustering for Fleet-Wide Fault DiagnosisCode0
Secure Communication Model For Quantum Federated Learning: A Post Quantum Cryptography (PQC) FrameworkCode0
FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models0
Bayesian Federated Learning: A Survey0
Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM0
User-Centric Federated Learning: Trading off Wireless Resources for Personalization0
Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated LearningCode1
Blockchain-based Federated Learning with Secure Aggregation in Trusted Execution Environment for Internet-of-Things0
Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare ApplicationsCode1
More Communication Does Not Result in Smaller Generalization Error in Federated Learning0
FedPIDAvg: A PID controller inspired aggregation method for Federated Learning0
Multi-Source to Multi-Target Decentralized Federated Domain Adaptation0
Personalized Federated Learning via Gradient Modulation for Heterogeneous Text Summarization0
Universal Adversarial Backdoor Attacks to Fool Vertical Federated Learning in Cloud-Edge Collaboration0
Breaching FedMD: Image Recovery via Paired-Logits Inversion AttackCode0
Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning0
Joint Client Assignment and UAV Route Planning for Indirect-Communication Federated Learning0
Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applications0
Get Rid Of Your Trail: Remotely Erasing Backdoors in Federated Learning0
Federated Compositional Deep AUC Maximization0
Model Pruning Enables Localized and Efficient Federated Learning for Yield Forecasting and Data Sharing0
Federated Alternate Training (FAT): Leveraging Unannotated Data Silos in Federated Segmentation for Medical Imaging0
BadVFL: Backdoor Attacks in Vertical Federated Learning0
Learning to Transmit with Provable Guarantees in Wireless Federated LearningCode1
Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networks0
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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