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 351400 of 6771 papers

TitleStatusHype
Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT NetworksCode1
Federated Learning under Heterogeneous and Correlated Client AvailabilityCode1
Rethinking Federated Learning With Domain Shift: A Prototype ViewCode1
Efficient On-device Training via Gradient FilteringCode1
ScaleFL: Resource-Adaptive Federated Learning With Heterogeneous ClientsCode1
When Federated Learning Meets Pre-trained Language Models' Parameter-Efficient Tuning MethodsCode1
FedTADBench: Federated Time-Series Anomaly Detection BenchmarkCode1
Modeling Global Distribution for Federated Learning with Label Distribution SkewCode1
Tackling Data Heterogeneity in Federated Learning with Class PrototypesCode1
On the effectiveness of partial variance reduction in federated learning with heterogeneous dataCode1
FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model ExtractionCode1
FedALA: Adaptive Local Aggregation for Personalized Federated LearningCode1
Federated Learning for 5G Base Station Traffic ForecastingCode1
FedGS: Federated Graph-based Sampling with Arbitrary Client AvailabilityCode1
Knowledge-Aware Federated Active Learning with Non-IID DataCode1
Federated Learning on Non-IID Graphs via Structural Knowledge SharingCode1
A Dynamic Weighted Federated Learning for Android Malware ClassificationCode1
Fed-TDA: Federated Tabular Data Augmentation on Non-IID DataCode1
FedDCT: Federated Learning of Large Convolutional Neural Networks on Resource Constrained Devices using Divide and Collaborative TrainingCode1
DYNAFED: Tackling Client Data Heterogeneity with Global DynamicsCode1
FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous DataCode1
Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and ChallengesCode1
Federated Adaptive Prompt Tuning for Multi-Domain Collaborative LearningCode1
FedCL: Federated Multi-Phase Curriculum Learning to Synchronously Correlate User HeterogeneityCode1
Universal EHR Federated Learning FrameworkCode1
FedLesScan: Mitigating Stragglers in Serverless Federated LearningCode1
Enhancing Efficiency in Multidevice Federated Learning through Data SelectionCode1
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism DesignCode1
FedTP: Federated Learning by Transformer PersonalizationCode1
TorchFL: A Performant Library for Bootstrapping Federated Learning ExperimentsCode1
Fast-Convergent Federated Learning via Cyclic AggregationCode1
Machine Unlearning of Federated ClustersCode1
FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural NetworksCode1
SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model CommunicationCode1
FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated LearningCode1
Learning to Invert: Simple Adaptive Attacks for Gradient Inversion in Federated LearningCode1
FedForgery: Generalized Face Forgery Detection with Residual Federated LearningCode1
Where to Begin? On the Impact of Pre-Training and Initialization in Federated LearningCode1
Label Inference Attacks Against Vertical Federated LearningCode1
Few-Shot Model Agnostic Federated LearningCode1
CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated LearningCode1
Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated LearningCode1
TabLeak: Tabular Data Leakage in Federated LearningCode1
Federated Domain Generalization for Image Recognition via Cross-Client Style TransferCode1
Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated LearningCode1
Sparse Random Networks for Communication-Efficient Federated LearningCode1
Rethinking Data Heterogeneity in Federated Learning: Introducing a New Notion and Standard BenchmarksCode1
Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles Between Client Data SubspacesCode1
Characterizing Internal Evasion Attacks in Federated LearningCode1
Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer 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