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 401450 of 6771 papers

TitleStatusHype
Federated Learning of Generative Image Priors for MRI ReconstructionCode1
Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive OptimizationCode1
Federated Learning on Non-IID Graphs via Structural Knowledge SharingCode1
Flame: Simplifying Topology Extension in Federated LearningCode1
Federated Learning under Distributed Concept DriftCode1
Federated Learning under Heterogeneous and Correlated Client AvailabilityCode1
Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order OptimizationCode1
Federated Learning via Inexact ADMMCode1
Federated Learning via Posterior Averaging: A New Perspective and Practical AlgorithmsCode1
Personalized Federated Learning with Adaptive Batchnorm for HealthcareCode1
A Blockchain-based Decentralized Federated Learning Framework with Committee ConsensusCode1
Anomaly-Flow: A Multi-domain Federated Generative Adversarial Network for Distributed Denial-of-Service DetectionCode1
Adaptive Test-Time Personalization for Federated LearningCode1
Federated Learning with Extremely Noisy Clients via Negative DistillationCode1
FedAdapter: Efficient Federated Learning for Modern NLPCode1
Federated Learning with Partial Model PersonalizationCode1
Federated Learning with Quantum Computing and Fully Homomorphic Encryption: A Novel Computing Paradigm Shift in Privacy-Preserving MLCode1
FedDR -- Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite OptimizationCode1
Federated Modality-specific Encoders and Multimodal Anchors for Personalized Brain Tumor SegmentationCode1
Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image ClassificationCode1
Adapt to Adaptation: Learning Personalization for Cross-Silo Federated LearningCode1
Federated Multi-organ Segmentation with Inconsistent LabelsCode1
Auditing Privacy Defenses in Federated Learning via Generative Gradient LeakageCode1
Federated Neuromorphic Learning of Spiking Neural Networks for Low-Power Edge IntelligenceCode1
Analyzing Federated Learning through an Adversarial LensCode1
Federated Optimization Algorithms with Random Reshuffling and Gradient CompressionCode1
Federated Reconstruction: Partially Local Federated LearningCode1
Federated Robustness Propagation: Sharing Robustness in Heterogeneous Federated LearningCode1
TARGET: Federated Class-Continual Learning via Exemplar-Free DistillationCode1
Federated Self-Training for Semi-Supervised Audio RecognitionCode1
Federated Unlearning: A Survey on Methods, Design Guidelines, and Evaluation MetricsCode1
Federated Unlearning: How to Efficiently Erase a Client in FL?Code1
Federated Unsupervised Domain Generalization using Global and Local Alignment of GradientsCode1
FedFA: Federated Feature AugmentationCode1
Backdoor Attacks on Federated Learning with Lottery Ticket HypothesisCode1
FedFly: Towards Migration in Edge-based Distributed Federated LearningCode1
An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public TransportationCode1
FedForgery: Generalized Face Forgery Detection with Residual Federated LearningCode1
An Efficient Approach for Cross-Silo Federated Learning to RankCode1
An Efficient Framework for Clustered Federated LearningCode1
ARFED: Attack-Resistant Federated averaging based on outlier eliminationCode1
A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data SourcesCode1
FedGroup: Efficient Clustered Federated Learning via Decomposed Data-Driven MeasureCode1
FedHCA2: Towards Hetero-Client Federated Multi-Task LearningCode1
FEDIC: Federated Learning on Non-IID and Long-Tailed Data via Calibrated DistillationCode1
FedILC: Weighted Geometric Mean and Invariant Gradient Covariance for Federated Learning on Non-IID DataCode1
An In-Depth Evaluation of Federated Learning on Biomedical Natural Language ProcessingCode1
FedLesScan: Mitigating Stragglers in Serverless Federated LearningCode1
Attack of the Tails: Yes, You Really Can Backdoor Federated LearningCode1
Asynchronous Federated Continual 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