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 22512300 of 6771 papers

TitleStatusHype
Leveraging Federated Learning for Automatic Detection of Clopidogrel Treatment Failures0
Rethinking Clustered Federated Learning in NOMA Enhanced Wireless Networks0
PPS-QMIX: Periodically Parameter Sharing for Accelerating Convergence of Multi-Agent Reinforcement LearningCode0
Training Machine Learning models at the Edge: A Survey0
Towards Robust Federated Learning via Logits Calibration on Non-IID Data0
MeanCache: User-Centric Semantic Caching for LLM Web Services0
FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive ModelsCode0
Federated Learning Under Attack: Exposing Vulnerabilities through Data Poisoning Attacks in Computer NetworksCode0
Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks0
FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal DecouplingCode1
Leveraging Federated Learning and Edge Computing for Recommendation Systems within Cloud Computing Networks0
A Survey on Federated Unlearning: Challenges and Opportunities0
Towards Optimal Customized Architecture for Heterogeneous Federated Learning with Contrastive Cloud-Edge Model DecouplingCode0
A Hierarchical Federated Learning Approach for the Internet of Things0
Partial Federated Learning0
Asyn2F: An Asynchronous Federated Learning Framework with Bidirectional Model Aggregation0
A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications0
Enhancing Data Provenance and Model Transparency in Federated Learning Systems - A Database Approach0
Analysis of Privacy Leakage in Federated Large Language ModelsCode0
Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach0
Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey0
FedRDMA: Communication-Efficient Cross-Silo Federated LLM via Chunked RDMA Transmission0
Blockchain-empowered Federated Learning: Benefits, Challenges, and SolutionsCode0
Federated Learning via Lattice Joint Source-Channel Coding0
Cloud-based Federated Learning Framework for MRI Segmentation0
On the Convergence of Federated Learning Algorithms without Data SimilarityCode0
Federated Linear Contextual Bandits with Heterogeneous Clients0
Global and Local Prompts Cooperation via Optimal Transport for Federated LearningCode2
SPriFed-OMP: A Differentially Private Federated Learning Algorithm for Sparse Basis Recovery0
Decoupled Subgraph Federated LearningCode0
RobWE: Robust Watermark Embedding for Personalized Federated Learning Model Ownership Protection0
Improving Group Connectivity for Generalization of Federated Deep Learning0
Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data SilosCode0
CollaFuse: Navigating Limited Resources and Privacy in Collaborative Generative AICode0
PrivatEyes: Appearance-based Gaze Estimation Using Federated Secure Multi-Party Computation0
Auditable Homomorphic-based Decentralized Collaborative AI with Attribute-based Differential Privacy0
Impact of network topology on the performance of Decentralized Federated Learning0
Decentralised Traffic Incident Detection via Network Lasso0
Communication Efficient ConFederated Learning: An Event-Triggered SAGA Approach0
Federated Learning for Estimating Heterogeneous Treatment Effects0
FedBRB: An Effective Solution to the Small-to-Large Scenario in Device-Heterogeneity Federated Learning0
FedUV: Uniformity and Variance for Heterogeneous Federated Learning0
FedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-supervised Medical Image SegmentationCode1
Multiple Access in the Era of Distributed Computing and Edge Intelligence0
MIP: CLIP-based Image Reconstruction from PEFT Gradients0
FedReview: A Review Mechanism for Rejecting Poisoned Updates in Federated Learning0
Watch Your Head: Assembling Projection Heads to Save the Reliability of Federated ModelsCode0
BlockFUL: Enabling Unlearning in Blockchained Federated Learning0
Trustworthy Personalized Bayesian Federated Learning via Posterior Fine-Tune0
Distribution-Free Fair Federated Learning with Small Samples0
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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