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 151200 of 6771 papers

TitleStatusHype
Differentially Private Learning with Adaptive ClippingCode1
Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance ReductionCode1
Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer LearningCode1
Analyzing Federated Learning through an Adversarial LensCode1
Communication Efficient and Provable Federated UnlearningCode1
Communication-Efficient Federated Learning with Accelerated Client GradientCode1
Collaborative Fairness in Federated LearningCode1
Communication-Efficient Diffusion Strategy for Performance Improvement of Federated Learning with Non-IID DataCode1
An Efficient Framework for Clustered Federated LearningCode1
Adaptive and Parallel Split Federated Learning in Vehicular Edge ComputingCode1
An Empirical Study of Personalized Federated LearningCode1
Communication-Efficient Federated Learning with Compensated Overlap-FedAvgCode1
CoDeC: Communication-Efficient Decentralized Continual LearningCode1
A New Federated Learning Framework Against Gradient Inversion AttacksCode1
Communication-Efficient Learning of Deep Networks from Decentralized DataCode1
Communication-Efficient Stochastic Zeroth-Order Optimization for Federated LearningCode1
Combating Exacerbated Heterogeneity for Robust Models in Federated LearningCode1
Anomaly-Flow: A Multi-domain Federated Generative Adversarial Network for Distributed Denial-of-Service DetectionCode1
A Hybrid Self-Supervised Learning Framework for Vertical Federated LearningCode1
Agnostic Federated LearningCode1
APPFL: Open-Source Software Framework for Privacy-Preserving Federated LearningCode1
APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a ServiceCode1
A Practical Recipe for Federated Learning Under Statistical Heterogeneity Experimental DesignCode1
DENSE: Data-Free One-Shot Federated LearningCode1
CoCoFL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and QuantizationCode1
A Survey for Federated Learning Evaluations: Goals and MeasuresCode1
Acceleration of Federated Learning with Alleviated Forgetting in Local TrainingCode1
ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret SharingCode1
Data Poisoning Attacks Against Federated Learning SystemsCode1
Data Valuation and Detections in Federated LearningCode1
ACCO: Accumulate While You Communicate for Communication-Overlapped Sharded LLM TrainingCode1
Decentralized Federated Learning: Balancing Communication and Computing CostsCode1
Accumulative Poisoning Attacks on Real-time DataCode1
Adaptive Federated OptimizationCode1
Communication-Efficient Adaptive Federated LearningCode1
Deep Federated Learning for Autonomous DrivingCode1
Asynchronous Federated Continual LearningCode1
A Survey on Vulnerability of Federated Learning: A Learning Algorithm PerspectiveCode1
Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT NetworksCode1
Detecting Backdoor Attacks in Federated Learning via Direction Alignment InspectionCode1
A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data SourcesCode1
Attack of the Tails: Yes, You Really Can Backdoor Federated LearningCode1
Communication Efficient Federated Learning for Multilingual Neural Machine Translation with AdapterCode1
Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-offCode1
ALI-DPFL: Differentially Private Federated Learning with Adaptive Local IterationsCode1
A Better Alternative to Error Feedback for Communication-Efficient Distributed LearningCode1
DistFL: Distribution-aware Federated Learning for Mobile ScenariosCode1
Client-Level Differential Privacy via Adaptive Intermediary in Federated Medical ImagingCode1
CLIP-guided Federated Learning on Heterogeneous and Long-Tailed DataCode1
Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated LearningCode1
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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