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 251300 of 6771 papers

TitleStatusHype
FedGiA: An Efficient Hybrid Algorithm for Federated LearningCode1
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding AggregationCode1
Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial IntelligenceCode1
Federated Learning Enables Big Data for Rare Cancer Boundary DetectionCode1
Efficient On-device Training via Gradient FilteringCode1
Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth LandscapeCode1
A Survey on Vulnerability of Federated Learning: A Learning Algorithm PerspectiveCode1
EasyFL: A Low-code Federated Learning Platform For DummiesCode1
DYNAFED: Tackling Client Data Heterogeneity with Global DynamicsCode1
FedAdapter: Efficient Federated Learning for Modern NLPCode1
Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class ImbalanceCode1
Edge Federated Learning Via Unit-Modulus Over-The-Air ComputationCode1
Efficient passive membership inference attack in federated learningCode1
FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated LearningCode1
BAFFLE: A Baseline of Backpropagation-Free Federated LearningCode1
Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient DescentCode1
Dopamine: Differentially Private Federated Learning on Medical DataCode1
ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret SharingCode1
APPFL: Open-Source Software Framework for Privacy-Preserving Federated LearningCode1
DENSE: Data-Free One-Shot Federated LearningCode1
DistFL: Distribution-aware Federated Learning for Mobile ScenariosCode1
Anomaly-Flow: A Multi-domain Federated Generative Adversarial Network for Distributed Denial-of-Service DetectionCode1
Differentially Private Vertical Federated ClusteringCode1
Differentially Private Federated Learning on Heterogeneous DataCode1
Differentially Private Federated Learning: A Client Level PerspectiveCode1
Differentially Private Learning with Adaptive ClippingCode1
DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse TrainingCode1
A New Federated Learning Framework Against Gradient Inversion AttacksCode1
Detecting Backdoor Attacks in Federated Learning via Direction Alignment InspectionCode1
Device Heterogeneity in Federated Learning: A Superquantile ApproachCode1
APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a ServiceCode1
Applied Federated Learning: Improving Google Keyboard Query SuggestionsCode1
Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked VehiclesCode1
A Practical Recipe for Federated Learning Under Statistical Heterogeneity Experimental DesignCode1
A Federated Data-Driven Evolutionary AlgorithmCode1
A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime DetectionCode1
ACTION: Augmentation and Computation Toolbox for Brain Network Analysis with Functional MRICode1
Attack of the Tails: Yes, You Really Can Backdoor Federated LearningCode1
A federated graph neural network framework for privacy-preserving personalizationCode1
A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and ComparisonCode1
A Survey for Federated Learning Evaluations: Goals and MeasuresCode1
Exploring Federated Unlearning: Review, Comparison, and InsightsCode1
Dynamic Defense Against Byzantine Poisoning Attacks in Federated LearningCode1
An Empirical Study of Personalized Federated LearningCode1
Active Membership Inference Attack under Local Differential Privacy in Federated LearningCode1
A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data SourcesCode1
Asynchronous Federated Continual LearningCode1
Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT NetworksCode1
Defending against Backdoors in Federated Learning with Robust Learning RateCode1
DESTRESS: Computation-Optimal and Communication-Efficient Decentralized Nonconvex Finite-Sum OptimizationCode1
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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