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
Relay-Assisted Cooperative Federated LearningCode1
DeceFL: A Principled Decentralized Federated Learning FrameworkCode1
Federated Self-Training for Semi-Supervised Audio RecognitionCode1
FedAdapt: Adaptive Offloading for IoT Devices in Federated LearningCode1
Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platformCode1
RoFL: Robustness of Secure Federated LearningCode1
Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular DiseaseCode1
SplitAVG: A heterogeneity-aware federated deep learning method for medical imagingCode1
On Bridging Generic and Personalized Federated Learning for Image ClassificationCode1
Gradient-Leakage Resilient Federated LearningCode1
FedMix: Approximation of Mixup under Mean Augmented Federated LearningCode1
FedCMR: Federated Cross-Modal RetrievalCode1
Personalized Federated Learning with Gaussian ProcessesCode1
Benchmarking Differential Privacy and Federated Learning for BERT ModelsCode1
Federated Graph Classification over Non-IID GraphsCode1
Subgraph Federated Learning with Missing Neighbor GenerationCode1
An Efficient Approach for Cross-Silo Federated Learning to RankCode1
Federated Learning with Positive and Unlabeled DataCode1
FedCM: Federated Learning with Client-level MomentumCode1
Soteria: Provable Defense Against Privacy Leakage in Federated Learning From Representation PerspectiveCode1
Accumulative Poisoning Attacks on Real-time DataCode1
Federated Robustness Propagation: Sharing Robustness in Heterogeneous Federated LearningCode1
Optimality and Stability in Federated Learning: A Game-theoretic ApproachCode1
Optimal Accounting of Differential Privacy via Characteristic FunctionCode1
Federated Semi-supervised Medical Image Classification via Inter-client Relation MatchingCode1
CRFL: Certifiably Robust Federated Learning against Backdoor AttacksCode1
Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data DetectionCode1
Federated Learning with Spiking Neural NetworksCode1
A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated LearningCode1
Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant MatrixCode1
Rethinking Architecture Design for Tackling Data Heterogeneity in Federated LearningCode1
Cross-Node Federated Graph Neural Network for Spatio-Temporal Data ModelingCode1
FL-Market: Trading Private Models in Federated LearningCode1
Fast Federated Learning in the Presence of Arbitrary Device UnavailabilityCode1
Preservation of the Global Knowledge by Not-True Distillation in Federated LearningCode1
SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural NetworksCode1
FedBABU: Towards Enhanced Representation for Federated Image ClassificationCode1
SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate TrainingCode1
Meta-HAR: Federated Representation Learning for Human Activity RecognitionCode1
Quantum Federated Learning with Quantum DataCode1
FedScale: Benchmarking Model and System Performance of Federated Learning at ScaleCode1
Fast Federated Learning by Balancing Communication Trade-OffsCode1
HyFed: A Hybrid Federated Framework for Privacy-preserving Machine LearningCode1
Data-Free Knowledge Distillation for Heterogeneous Federated LearningCode1
EasyFL: A Low-code Federated Learning Platform For DummiesCode1
Node Selection Toward Faster Convergence for Federated Learning on Non-IID DataCode1
The Federated Tumor Segmentation (FeTS) ChallengeCode1
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated LearningCode1
Separate but Together: Unsupervised Federated Learning for Speech Enhancement from Non-IID DataCode1
Personalized Federated Learning by Structured and Unstructured Pruning under Data HeterogeneityCode1
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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