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 39514000 of 6771 papers

TitleStatusHype
When Do Curricula Work in Federated Learning?Code0
Graph Federated Learning with Hidden Representation Sharing0
Deep Unfolding-based Weighted Averaging for Federated Learning in Heterogeneous Environments0
AsyncFLEO: Asynchronous Federated Learning for LEO Satellite Constellations with High-Altitude Platforms0
Federated Learning -- Methods, Applications and beyond0
Personalized Decentralized Multi-Task Learning Over Dynamic Communication Graphs0
Free-Rider Games for Federated Learning with Selfish Clients in NextG Wireless Networks0
When Federated Learning Meets Pre-trained Language Models' Parameter-Efficient Tuning MethodsCode1
Adaptive Control of Client Selection and Gradient Compression for Efficient Federated Learning0
FedTADBench: Federated Time-Series Anomaly Detection BenchmarkCode1
AI Security for Geoscience and Remote Sensing: Challenges and Future Trends0
Plankton-FL: Exploration of Federated Learning for Privacy-Preserving Training of Deep Neural Networks for Phytoplankton Classification0
On Noisy Evaluation in Federated Hyperparameter TuningCode0
Personalized Federated Recommender Systems with Private and Partially Federated AutoEncoders0
Toward Data Heterogeneity of Federated Learning0
Modeling Global Distribution for Federated Learning with Label Distribution SkewCode1
FewFedWeight: Few-shot Federated Learning Framework across Multiple NLP Tasks0
SplitGP: Achieving Both Generalization and Personalization in Federated Learning0
Federated Learning with Flexible Control0
Mobile Augmented Reality with Federated Learning in the Metaverse0
Robust Learning Protocol for Federated Tumor Segmentation Challenge0
White-box Inference Attacks against Centralized Machine Learning and Federated Learning0
Deep leakage from gradients0
FedSkip: Combatting Statistical Heterogeneity with Federated Skip AggregationCode0
Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networks0
FLAGS Framework for Comparative Analysis of Federated Learning Algorithms0
Hierarchical Over-the-Air FedGradNorm0
Bayesian data fusion with shared priors0
Robust Split Federated Learning for U-shaped Medical Image NetworksCode0
AFLGuard: Byzantine-robust Asynchronous Federated Learning0
Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMO0
Collaborating Heterogeneous Natural Language Processing Tasks via Federated Learning0
ResFed: Communication Efficient Federated Learning by Transmitting Deep Compressed Residuals0
Federated Learning for Inference at Anytime and Anywhere0
Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential PrivacyCode0
GTFLAT: Game Theory Based Add-On For Empowering Federated Learning Aggregation TechniquesCode0
Reconstructing Training Data from Model Gradient, Provably0
MOB-FL: Mobility-Aware Federated Learning for Intelligent Connected Vehicles0
Towards Fleet-wide Sharing of Wind Turbine Condition Information through Privacy-preserving Federated Learning0
PaDPaF: Partial Disentanglement with Partially-Federated GANsCode0
Tackling Data Heterogeneity in Federated Learning with Class PrototypesCode1
Encrypted machine learning of molecular quantum propertiesCode0
Unexpectedly Useful: Convergence Bounds And Real-World Distributed Learning0
HierarchyFL: Heterogeneous Federated Learning via Hierarchical Self-Distillation0
Differentiated Federated Reinforcement Learning Based Traffic Offloading on Space-Air-Ground Integrated Networks0
Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning0
Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics0
On the effectiveness of partial variance reduction in federated learning with heterogeneous dataCode1
Distributed Stochastic Gradient Descent with Cost-Sensitive and Strategic Agents0
Distributed Pruning Towards Tiny Neural Networks in Federated Learning0
Show:102550
← PrevPage 80 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