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 59516000 of 6771 papers

TitleStatusHype
Federated Survival ForestsCode0
Speed Up Federated Learning in Heterogeneous Environment: A Dynamic Tiering ApproachCode0
Adversarially Guided Stateful Defense Against Backdoor Attacks in Federated Deep LearningCode0
Cross-client Label Propagation for Transductive and Semi-Supervised Federated LearningCode0
BOBA: Byzantine-Robust Federated Learning with Label SkewnessCode0
Federated Submodel Optimization for Hot and Cold Data FeaturesCode0
Learn What You Need in Personalized Federated LearningCode0
LEFL: Low Entropy Client Sampling in Federated LearningCode0
Communication Efficient and Privacy-Preserving Federated Learning Based on Evolution StrategiesCode0
Federated Stain Normalization for Computational PathologyCode0
FedQAS: Privacy-aware machine reading comprehension with federated learningCode0
Communication-Efficient ADMM-based Federated LearningCode0
Less is More: A privacy-respecting Android malware classifier using Federated LearningCode0
Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data HeterogeneityCode0
Blockchain-empowered Federated Learning: Benefits, Challenges, and SolutionsCode0
FedQV: Leveraging Quadratic Voting in Federated LearningCode0
Federated Spectral Graph Transformers Meet Neural Ordinary Differential Equations for Non-IID GraphsCode0
Fed-RAC: Resource-Aware Clustering for Tackling Heterogeneity of Participants in Federated LearningCode0
Scaffold with Stochastic Gradients: New Analysis with Linear Speed-UpCode0
Leverage Variational Graph Representation For Model Poisoning on Federated LearningCode0
Lottery Aware Sparsity Hunting: Enabling Federated Learning on Resource-Limited EdgeCode0
FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous VehiclesCode0
Proof-of-Collaborative-Learning: A Multi-winner Federated Learning Consensus AlgorithmCode0
The Cost of Local and Global Fairness in Federated LearningCode0
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure AggregationCode0
Federated singular value decomposition for high dimensional dataCode0
A Hybrid Approach to Privacy-Preserving Federated LearningCode0
FedRec: Federated Learning of Universal Receivers over Fading ChannelsCode0
Wyner-Ziv Estimators for Distributed Mean Estimation with Side Information and OptimizationCode0
Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax GuaranteesCode0
FedRef: Communication-Efficient Bayesian Fine Tuning with Reference ModelCode0
Scalable Data Point Valuation in Decentralized LearningCode0
FedReMa: Improving Personalized Federated Learning via Leveraging the Most Relevant ClientsCode0
User Consented Federated Recommender System Against Personalized Attribute Inference AttackCode0
Parameter-Efficient Fine-Tuning without Introducing New LatencyCode0
URVFL: Undetectable Data Reconstruction Attack on Vertical Federated LearningCode0
Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated LearningCode0
Differentially Private Federated Learning for Cancer PredictionCode0
The Effect of Training Parameters and Mechanisms on Decentralized Federated Learning based on MNIST DatasetCode0
Towards Fair and Privacy-Preserving Federated Deep ModelsCode0
Asynchronous Federated Learning: A Scalable Approach for Decentralized Machine LearningCode0
FedRIR: Rethinking Information Representation in Federated LearningCode0
FedSECA: Sign Election and Coordinate-wise Aggregation of Gradients for Byzantine Tolerant Federated LearningCode0
FedRKG: A Privacy-preserving Federated Recommendation Framework via Knowledge Graph EnhancementCode0
FedRN: Exploiting k-Reliable Neighbors Towards Robust Federated LearningCode0
PaDPaF: Partial Disentanglement with Partially-Federated GANsCode0
Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things ApplicationCode0
Federated Semi-Supervised Learning with Prototypical NetworksCode0
LiD-FL: Towards List-Decodable Federated LearningCode0
Scale Federated Learning for Label Set Mismatch in Medical Image ClassificationCode0
Show:102550
← PrevPage 120 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