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 28512900 of 6771 papers

TitleStatusHype
Artificial Intelligence-Driven Clinical Decision Support Systems0
Federated Learning over Noisy Channels: Convergence Analysis and Design Examples0
Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysis0
Federated Learning over Wireless Fading Channels0
Federated Learning over Wireless IoT Networks with Optimized Communication and Resources0
FedCode: Communication-Efficient Federated Learning via Transferring Codebooks0
Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approach0
Federated Learning Priorities Under the European Union Artificial Intelligence Act0
The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscape0
Federated Learning Robust to Byzantine Attacks: Achieving Zero Optimality Gap0
A Unified Linear Speedup Analysis of Federated Averaging and Nesterov FedAvg0
Federated Learning Strategies for Coordinated Beamforming in Multicell ISAC0
Federated Learning: Strategies for Improving Communication Efficiency0
Decentralized EM to Learn Gaussian Mixtures from Datasets Distributed by Features0
Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations0
A Graph Federated Architecture with Privacy Preserving Learning0
Federated Learning under Attack: Improving Gradient Inversion for Batch of Images0
Federated Learning under Covariate Shifts with Generalization Guarantees0
Communication-Efficient Federated Bilevel Optimization with Local and Global Lower Level Problems0
Self-supervised On-device Federated Learning from Unlabeled Streams0
Federated Learning under Importance Sampling0
Communication-Efficient Federated Distillation with Active Data Sampling0
Decentralized Federated Learning: A Survey on Security and Privacy0
Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning0
A Federated Deep Learning Framework for Cell-Free RSMA Networks0
Accelerating Fair Federated Learning: Adaptive Federated Adam0
Federated Learning with Projected Trajectory Regularization0
Federated Learning using Smart Contracts on Blockchains, based on Reward Driven Approach0
Federated Learning Using Three-Operator ADMM0
Federated Learning Using Variance Reduced Stochastic Gradient for Probabilistically Activated Agents0
Federated Learning Versus Classical Machine Learning: A Convergence Comparison0
FedNMUT -- Federated Noisy Model Update Tracking Convergence Analysis0
Federated Learning With Quantized Global Model Updates0
Federated Learning via Indirect Server-Client Communications0
Federated Learning with Sample-level Client Drift Mitigation0
Federated Neural Compression Under Heterogeneous Data0
Federated Learning via Intelligent Reflecting Surface0
Federated Learning via Lattice Joint Source-Channel Coding0
Federated Learning via Over-the-Air Computation0
Decentralized Federated Learning Preserves Model and Data Privacy0
Communication-Efficient Federated Distillation0
Federated Learning via Synthetic Data0
Federated Learning via Unmanned Aerial Vehicle0
Federated Learning via Variational Bayesian Inference: Personalization, Sparsity and Clustering0
Federated Learning While Providing Model as a Service: Joint Training and Inference Optimization0
FedCME: Client Matching and Classifier Exchanging to Handle Data Heterogeneity in Federated Learning0
Decentralized Federated Learning with Model Caching on Mobile Agents0
Federated Learning with a Sampling Algorithm under Isoperimetry0
Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Control0
FedClust: Tackling Data Heterogeneity in Federated Learning through Weight-Driven Client Clustering0
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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