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 66516700 of 6771 papers

TitleStatusHype
A Universal Metric of Dataset Similarity for Cross-silo Federated LearningCode0
SkyMask: Attack-agnostic Robust Federated Learning with Fine-grained Learnable MasksCode0
CalFAT: Calibrated Federated Adversarial Training with Label SkewnessCode0
Robust Aggregation for Federated LearningCode0
FedCos: A Scene-adaptive Federated Optimization Enhancement for Performance ImprovementCode0
Privacy-Preserving Distributed Learning for Residential Short-Term Load ForecastingCode0
Robust and Communication-Efficient Federated Learning from Non-IID DataCode0
Privacy-Preserving Edge Federated Learning for Intelligent Mobile-Health SystemsCode0
Robust and Communication-Efficient Federated Domain Adaptation via Random FeaturesCode0
Robust and Differentially Private Mean EstimationCode0
Anchor Sampling for Federated Learning with Partial Client ParticipationCode0
UFGraphFR: An attempt at a federated recommendation system based on user text characteristicsCode0
Robust Asymmetric Heterogeneous Federated Learning with Corrupted ClientsCode0
On Noisy Evaluation in Federated Hyperparameter TuningCode0
On the Importance and Applicability of Pre-Training for Federated LearningCode0
CADIS: Handling Cluster-skewed Non-IID Data in Federated Learning with Clustered Aggregation and Knowledge DIStilled RegularizationCode0
Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated LearningCode0
In-depth Analysis of Privacy Threats in Federated Learning for Medical DataCode0
Towards the efficacy of federated prediction for epidemics on networksCode0
Individualized Federated Learning for Traffic Prediction with Error Driven AggregationCode0
Privacy Preserving Federated Learning in Medical Imaging with Uncertainty EstimationCode0
On the Byzantine-Resilience of Distillation-Based Federated LearningCode0
An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal EffectsCode0
A Federated Learning Benchmark for Drug-Target InteractionCode0
On the Convergence of Clustered Federated LearningCode0
Infinitely Divisible Noise in the Low Privacy RegimeCode0
A Federated Data-Driven Evolutionary Algorithm for Expensive Multi/Many-objective OptimizationCode0
InFL-UX: A Toolkit for Web-Based Interactive Federated LearningCode0
On the Convergence of Decentralized Federated Learning Under Imperfect Information SharingCode0
DROP: Poison Dilution via Knowledge Distillation for Federated LearningCode0
A Unified Solution to Diverse Heterogeneities in One-shot Federated LearningCode0
On the Convergence of Federated Learning Algorithms without Data SimilarityCode0
Informed Greedy Algorithm for Scalable Bayesian Network Fusion via Minimum Cut AnalysisCode0
A Method to Reveal Speaker Identity in Distributed ASR Training, and How to Counter ItCode0
A Lightweight and Secure Deep Learning Model for Privacy-Preserving Federated Learning in Intelligent EnterprisesCode0
Robust Federated Learning Against Poisoning Attacks: A GAN-Based Defense FrameworkCode0
Robust Federated Learning by Mixture of ExpertsCode0
UFPS: A unified framework for partially-annotated federated segmentation in heterogeneous data distributionCode0
Instance-wise Batch Label Restoration via Gradients in Federated LearningCode0
Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated LearningCode0
Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and RegressionCode0
A Joint Learning and Communications Framework for Federated Learning over Wireless NetworksCode0
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent KernelsCode0
ULDP-FL: Federated Learning with Across Silo User-Level Differential PrivacyCode0
Intelligent Client Selection for Federated Learning using Cellular AutomataCode0
On the Efficacy of Differentially Private Few-shot Image ClassificationCode0
On the Efficiency of Privacy Attacks in Federated LearningCode0
Technical Insights and Legal Considerations for Advancing Federated Learning in BioinformaticsCode0
A Federated Approach to Predicting Emojis in Hindi TweetsCode0
Towards Understanding Quality Challenges of the Federated Learning for Neural Networks: A First Look from the Lens of RobustnessCode0
Show:102550
← PrevPage 134 of 136Next →

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