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 46014650 of 6771 papers

TitleStatusHype
From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challenges0
From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare0
From Competition to Collaboration: Making Toy Datasets on Kaggle Clinically Useful for Chest X-Ray Diagnosis Using Federated Learning0
From Distributed Machine Learning to Federated Learning: A Survey0
From Federated to Fog Learning: Distributed Machine Learning over Heterogeneous Wireless Networks0
From Federated Learning to Quantum Federated Learning for Space-Air-Ground Integrated Networks0
Exact and Linear Convergence for Federated Learning under Arbitrary Client Participation is Attainable0
From Learning to Analytics: Improving Model Efficacy with Goal-Directed Client Selection0
From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling0
From Local SGD to Local Fixed-Point Methods for Federated Learning0
From Local SGD to Local Fixed Point Methods for Federated Learning0
From Models to Network Topologies: A Topology Inference Attack in Decentralized Federated Learning0
Meta Matrix Factorization for Federated Rating Predictions0
FSAR: Federated Skeleton-based Action Recognition with Adaptive Topology Structure and Knowledge Distillation0
FSL: Federated Supermask Learning0
FRL: Federated Rank Learning0
FS-Real: Towards Real-World Cross-Device Federated Learning0
FSSUAVL: A Discriminative Framework using Vision Models for Federated Self-Supervised Audio and Image Understanding0
FTA: Stealthy and Adaptive Backdoor Attack with Flexible Triggers on Federated Learning0
Model Inversion Attack against Federated Unlearning0
Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learning0
Secure Embedding Aggregation for Federated Representation Learning0
Fundamental Limits of Hierarchical Secure Aggregation with Cyclic User Association0
Fuse and Federate: Enhancing EV Charging Station Security with Multimodal Fusion and Federated Learning0
Fusion Learning: A One Shot Federated Learning0
Fusion of Federated Learning and Industrial Internet of Things: A Survey0
Future-Proofing Medical Imaging with Privacy-Preserving Federated Learning and Uncertainty Quantification: A Review0
G^2uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering0
GAI-Enabled Explainable Personalized Federated Semi-Supervised Learning0
Game of Privacy: Towards Better Federated Platform Collaboration under Privacy Restriction0
How Can Incentives and Cut Layer Selection Influence Data Contribution in Split Federated Learning?0
GAN-based Vertical Federated Learning for Label Protection in Binary Classification0
GANcrop: A Contrastive Defense Against Backdoor Attacks in Federated Learning0
GC-Fed: Gradient Centralized Federated Learning with Partial Client Participation0
GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS0
GeFL: Model-Agnostic Federated Learning with Generative Models0
GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning0
Minimax Excess Risk of First-Order Methods for Statistical Learning with Data-Dependent Oracles0
Generalization in Federated Learning: A Conditional Mutual Information Framework0
Generalized and Personalized Federated Learning with Foundation Models via Orthogonal Transformations0
Generalized Policy Learning for Smart Grids: FL TRPO Approach0
Generation of Synthetic Electronic Health Records Using a Federated GAN0
Generative AI like ChatGPT in Blockchain Federated Learning: use cases, opportunities and future0
Generative Data Augmentation for Non-IID Problem in Decentralized Clinical Machine Learning0
Geographical Node Clustering and Grouping to Guarantee Data IIDness in Federated Learning0
Get Rid Of Your Trail: Remotely Erasing Backdoors in Federated Learning0
GFCL: A GRU-based Federated Continual Learning Framework against Data Poisoning Attacks in IoV0
γ-FedHT: Stepsize-Aware Hard-Threshold Gradient Compression in Federated Learning0
GFL: A Decentralized Federated Learning Framework Based On Blockchain0
GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learning0
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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