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 38513900 of 6771 papers

TitleStatusHype
Reliable Federated Disentangling Network for Non-IID Domain FeatureCode0
FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation0
Federated Learning for Water Consumption Forecasting in Smart Cities0
Regularized Weight Aggregation in Networked Federated Learning for Glioblastoma SegmentationCode0
Entropy-driven Fair and Effective Federated Learning0
FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust ClusteringCode0
Heterogeneous Datasets for Federated Survival Analysis SimulationCode0
CyclicFL: A Cyclic Model Pre-Training Approach to Efficient Federated Learning0
BAFFLE: A Baseline of Backpropagation-Free Federated LearningCode1
SplitGNN: Splitting GNN for Node Classification with Heterogeneous Attention0
FedPH: Privacy-enhanced Heterogeneous Federated LearningCode0
Uplink Scheduling in Federated Learning: an Importance-Aware Approach via Graph Representation Learning0
SuperFedNAS: Cost-Efficient Federated Neural Architecture Search for On-Device Inference0
Personalised Federated Learning On Heterogeneous Feature Spaces0
Time-sensitive Learning for Heterogeneous Federated Edge Intelligence0
Federated Learning over Coupled Graphs0
Interaction-level Membership Inference Attack Against Federated Recommender Systems0
When to Trust Aggregated Gradients: Addressing Negative Client Sampling in Federated Learning0
Integrating Local Real Data with Global Gradient Prototypes for Classifier Re-Balancing in Federated Long-Tailed Learning0
When does the student surpass the teacher? Federated Semi-supervised Learning with Teacher-Student EMA0
PolarAir: A Compressed Sensing Scheme for Over-the-Air Federated Learning0
Backdoor Attacks in Peer-to-Peer Federated Learning0
BayBFed: Bayesian Backdoor Defense for Federated Learning0
Federated Sufficient Dimension Reduction Through High-Dimensional Sparse Sliced Inverse Regression0
M22: A Communication-Efficient Algorithm for Federated Learning Inspired by Rate-DistortionCode0
Accelerating Fair Federated Learning: Adaptive Federated Adam0
FedExP: Speeding Up Federated Averaging via ExtrapolationCode0
Combined Use of Federated Learning and Image Encryption for Privacy-Preserving Image Classification with Vision Transformer0
Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological DataCode1
Energy Prediction using Federated LearningCode0
Federated Recommendation with Additive PersonalizationCode1
How Potent are Evasion Attacks for Poisoning Federated Learning-Based Signal Classifiers?0
A Communication-Efficient Adaptive Algorithm for Federated Learning under Cumulative Regret0
The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation0
Remote patient monitoring using artificial intelligence: Current state, applications, and challenges0
On the Vulnerability of Backdoor Defenses for Federated LearningCode1
Federated Automatic Differentiation0
Robust Knowledge Adaptation for Federated Unsupervised Person ReID0
Label Inference Attack against Split Learning under Regression SettingCode0
Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes0
SegViz: A federated-learning based framework for multi-organ segmentation on heterogeneous data sets with partial annotations0
Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT NetworksCode1
FedCliP: Federated Learning with Client Pruning0
From Isolation to Collaboration: Federated Class-Heterogeneous Learning for Chest X-Ray ClassificationCode0
HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association0
FedSepsis: A Federated Multi-Modal Deep Learning-Based Internet of Medical Things Application for Early Detection of Sepsis from Electronic Health Records Using Raspberry Pi and Jetson Nano DevicesCode0
FedSSC: Shared Supervised-Contrastive Federated Learning0
Poisoning Attacks and Defenses in Federated Learning: A Survey0
Jamming Attacks on Decentralized Federated Learning in General Multi-Hop Wireless Networks0
Federated Transfer-Ordered-Personalized Learning for Driver Monitoring Application0
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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