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

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Showing 5801–5850 of 6771 papers

TitleStatusHype
Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach—0
Client Selection in Federated Learning: Principles, Challenges, and Opportunities—0
Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies—0
Client Selection in Federated Learning based on Gradients Importance—0
Client Selection in Federated Learning with Data Heterogeneity and Network Latencies—0
Client Selection Strategies for Federated Semantic Communications in Heterogeneous IoT Networks—0
Client-side Gradient Inversion Against Federated Learning from Poisoning—0
Client-Side Patching against Backdoor Attacks in Federated Learning—0
Client-supervised Federated Learning: Towards One-model-for-all Personalization—0
Clinnova Federated Learning Proof of Concept: Key Takeaways from a Cross-border Collaboration—0
CLLoRA: An Approach to Measure the Effects of the Context Length for LLM Fine-Tuning—0
Closing the Gap between Client and Global Model Performance in Heterogeneous Federated Learning—0
Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentation—0
Cloud-based Federated Boosting for Mobile Crowdsensing—0
Cloud-based Federated Learning Framework for MRI Segmentation—0
Cloud-Magnetic Resonance Imaging System: In the Era of 6G and Artificial Intelligence—0
ClusMFL: A Cluster-Enhanced Framework for Modality-Incomplete Multimodal Federated Learning in Brain Imaging Analysis—0
Cluster-Aware Multi-Round Update for Wireless Federated Learning in Heterogeneous Environments—0
Cluster-Based Cooperative Digital Over-the-Air Aggregation for Wireless Federated Edge Learning—0
Cluster Based Secure Multi-Party Computation in Federated Learning for Histopathology Images—0
Cluster-driven Graph Federated Learning over Multiple Domains—0
Clustered Data Sharing for Non-IID Federated Learning over Wireless Networks—0
Clustered FedStack: Intermediate Global Models with Bayesian Information Criterion—0
Clustered Scheduling and Communication Pipelining For Efficient Resource Management Of Wireless Federated Learning—0
Clustering Algorithm to Detect Adversaries in Federated Learning—0
Clustering-Based Evolutionary Federated Multiobjective Optimization and Learning—0
CNN-FL for Biotechnology Industry Empowered by Internet-of-BioNano Things and Digital Twins—0
CoAst: Validation-Free Contribution Assessment for Federated Learning based on Cross-Round Valuation—0
Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning using Independent Component Analysis—0
Coded Computing for Federated Learning at the Edge—0
Coded Computing for Low-Latency Federated Learning over Wireless Edge Networks—0
Coded Federated Learning—0
Federated Learning Framework with Straggling Mitigation and Privacy-Awareness for AI-based Mobile Application Services—0
Coded Matrix Computations for D2D-enabled Linearized Federated Learning—0
CodedPaddedFL and CodedSecAgg: Straggler Mitigation and Secure Aggregation in Federated Learning—0
Coding for Straggler Mitigation in Federated Learning—0
CoDream: Exchanging dreams instead of models for federated aggregation with heterogeneous models—0
Cognitive Learning-Aided Multi-Antenna Communications—0
Cohort Squeeze: Beyond a Single Communication Round per Cohort in Cross-Device Federated Learning—0
Collaborating Heterogeneous Natural Language Processing Tasks via Federated Learning—0
Collaboration in Participant-Centric Federated Learning: A Game-Theoretical Perspective—0
Federated and continual learning for classification tasks in a society of devices—0
Collaborative and Efficient Personalization with Mixtures of Adaptors—0
Collaborative and Federated Black-box Optimization: A Bayesian Optimization Perspective—0
Collaborative Batch Size Optimization for Federated Learning—0
Collaborative City Digital Twin For Covid-19 Pandemic: A Federated Learning Solution—0
Collaborative Content Moderation in the Fediverse—0
Collaborative Domain Blocking: Using federated NLP To Detect Malicious Domains—0
Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge—0
Collaborative Heterogeneous Causal Inference Beyond Meta-analysis—0
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All datasetsCityscapes heterogeneousLandmarks-User-160kCIFAR-100 (alpha=0, 10 clients per round)CIFAR-100 (alpha=0, 20 clients per round)CIFAR-100 (alpha=0.5, 10 clients per round)CIFAR-100 (alpha=0.5, 20 clients per round)CIFAR-100 (alpha=0.5, 5 clients per round)CIFAR-100 (alpha=0, 5 clients per round)CIFAR-100 (alpha=1000, 10 clients per round)CIFAR-100 (alpha=1000, 20 clients per round)CIFAR-100 (alpha=1000, 5 clients per round)CIFAR100 (alpha=0.3, 10 clients per round)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SiloBN + ASAMmIoU49.75—Unverified
2SiloBN + SAMmIoU49.1—Unverified
3SiloBNmIoU45.96—Unverified
4FedSAM + SWAmIoU43.42—Unverified
5FedASAM + SWAmIoU43.02—Unverified
6FedAvg + SWAmIoU42.48—Unverified
7FedASAMmIoU42.27—Unverified
8FedSAMmIoU41.22—Unverified
9FedAvgmIoU38.65—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAAcc@1-1262Clients68.32—Unverified
2FedSAM + SWAAcc@1-1262Clients68.12—Unverified
3FedAvg + SWAAcc@1-1262Clients67.52—Unverified
4FedASAMAcc@1-1262Clients64.23—Unverified
5FedSAMAcc@1-1262Clients63.72—Unverified
6FedAvgAcc@1-1262Clients61.91—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.64—Unverified
2FedASAMACC@1-100Clients39.76—Unverified
3FedSAM + SWAACC@1-100Clients39.51—Unverified
4FedSAMACC@1-100Clients36.93—Unverified
5FedAvgACC@1-100Clients36.74—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients41.62—Unverified
2FedASAMACC@1-100Clients40.81—Unverified
3FedSAM + SWAACC@1-100Clients39.24—Unverified
4FedAvgACC@1-100Clients38.59—Unverified
5FedSAMACC@1-100Clients38.56—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.72—Unverified
2FedSAM + SWAACC@1-100Clients46.76—Unverified
3FedASAMACC@1-100Clients46.58—Unverified
4FedSAMACC@1-100Clients44.84—Unverified
5FedAvgACC@1-100Clients41.27—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.27—Unverified
2FedASAMACC@1-100Clients47.78—Unverified
3FedSAM + SWAACC@1-100Clients46.47—Unverified
4FedSAMACC@1-100Clients46.05—Unverified
5FedAvgACC@1-100Clients42.17—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients49.17—Unverified
2FedSAM + SWAACC@1-100Clients47.96—Unverified
3FedASAMACC@1-100Clients45.61—Unverified
4FedSAMACC@1-100Clients44.73—Unverified
5FedAvgACC@1-100Clients40.43—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.01—Unverified
2FedSAM + SWAACC@1-100Clients39.3—Unverified
3FedASAMACC@1-100Clients36.04—Unverified
4FedSAMACC@1-100Clients31.04—Unverified
5FedAvgACC@1-100Clients30.25—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.97—Unverified
2FedASAM + SWAACC@1-100Clients54.79—Unverified
3FedSAM + SWAACC@1-100Clients53.67—Unverified
4FedSAMACC@1-100Clients53.39—Unverified
5FedAvgACC@1-100Clients50.25—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.5—Unverified
2FedSAM + SWAACC@1-100Clients54.36—Unverified
3FedASAM + SWAACC@1-100Clients54.1—Unverified
4FedSAMACC@1-100Clients53.97—Unverified
5FedAvgACC@1-100Clients50.66—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.81—Unverified
2FedSAMACC@1-100Clients54.01—Unverified
3FedSAM + SWAACC@1-100Clients53.9—Unverified
4FedASAM + SWAACC@1-100Clients53.86—Unverified
5FedAvgACC@1-100Clients49.92—Unverified
#ModelMetricClaimedVerifiedStatus
1AdaBestAverage Top-1 Accuracy56.2—Unverified