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 601650 of 6771 papers

TitleStatusHype
Communication-Efficient Stochastic Zeroth-Order Optimization for Federated LearningCode1
FedLP: Layer-wise Pruning Mechanism for Communication-Computation Efficient Federated LearningCode1
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated LearningCode1
Exploring Federated Unlearning: Review, Comparison, and InsightsCode1
FedMatch: Federated Learning Over Heterogeneous Question Answering DataCode1
FedMAX: Mitigating Activation Divergence for Accurate and Communication-Efficient Federated LearningCode1
Federated Learning of Gboard Language Models with Differential PrivacyCode1
DeceFL: A Principled Decentralized Federated Learning FrameworkCode1
Attribute Inference Attack of Speech Emotion Recognition in Federated Learning SettingsCode1
Federated Learning of Generative Image Priors for MRI ReconstructionCode1
Cross-Node Federated Graph Neural Network for Spatio-Temporal Data ModelingCode1
FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated LearningCode1
Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive OptimizationCode1
CRFL: Certifiably Robust Federated Learning against Backdoor AttacksCode1
Cross-Silo Prototypical Calibration for Federated Learning with Non-IID DataCode1
Data Poisoning Attacks Against Federated Learning SystemsCode1
DAGER: Exact Gradient Inversion for Large Language ModelsCode1
A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data SourcesCode1
TabLeak: Tabular Data Leakage in Federated LearningCode1
DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge DevicesCode1
Auditing Privacy Defenses in Federated Learning via Generative Gradient LeakageCode1
FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated LearningCode1
Data Valuation and Detections in Federated LearningCode1
FedPop: Federated Population-based Hyperparameter TuningCode1
An In-Depth Evaluation of Federated Learning on Biomedical Natural Language ProcessingCode1
DBA: Distributed Backdoor Attacks against Federated LearningCode1
Decentralized Federated Learning: Balancing Communication and Computing CostsCode1
FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image ClassificationCode1
Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and ChallengesCode1
FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model ExtractionCode1
Decentralized Federated Learning: A Segmented Gossip ApproachCode1
Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech RecognitionCode1
FedSIS: Federated Split Learning with Intermediate Representation Sampling for Privacy-preserving Generalized Face Presentation Attack DetectionCode1
Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank DecompositionCode1
FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization AccuracyCode1
FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated LearningCode1
FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data GenerationCode1
FedTADBench: Federated Time-Series Anomaly Detection BenchmarkCode1
Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT NetworksCode1
Differentially private cross-silo federated learningCode1
FedZero: Leveraging Renewable Excess Energy in Federated LearningCode1
A Comprehensive View of Personalized Federated Learning on Heterogeneous Clinical DatasetsCode1
Adaptive and Parallel Split Federated Learning in Vehicular Edge ComputingCode1
DESTRESS: Computation-Optimal and Communication-Efficient Decentralized Nonconvex Finite-Sum OptimizationCode1
A Framework for Energy and Carbon Footprint Analysis of Distributed and Federated Edge LearningCode1
Asynchronous Federated Continual LearningCode1
Attack of the Tails: Yes, You Really Can Backdoor Federated LearningCode1
Asynchronous Federated Learning for Edge-assisted Vehicular NetworksCode1
FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image ClassificationCode1
Analysis and Evaluation of Synchronous and Asynchronous FLchainCode1
Show:102550
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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