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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1001–1050 of 6771 papers

TitleStatusHype
An Optimization Framework for Federated Edge Learning—0
Centroid Approximation for Byzantine-Tolerant Federated Learning—0
Breaking the Memory Wall for Heterogeneous Federated Learning via Progressive Training—0
Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing—0
Certified Federated Adversarial Training—0
Certified Robustness for Free in Differentially Private Federated Learning—0
Addressing Spatial-Temporal Data Heterogeneity in Federated Continual Learning via Tail Anchor—0
CFedAvg: Achieving Efficient Communication and Fast Convergence in Non-IID Federated Learning—0
CFLIT: Coexisting Federated Learning and Information Transfer—0
CG-FedLLM: How to Compress Gradients in Federated Fune-tuning for Large Language Models—0
Breaking the centralized barrier for cross-device federated learning—0
Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Images—0
Breaking Secure Aggregation: Label Leakage from Aggregated Gradients in Federated Learning—0
Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning—0
An Empirical Study of Federated Prompt Learning for Vision Language Model—0
Characterization of the Global Bias Problem in Aerial Federated Learning—0
Chisme: Fully Decentralized Differentiated Deep Learning for Edge Intelligence—0
Chu-ko-nu: A Reliable, Efficient, and Anonymously Authentication-Enabled Realization for Multi-Round Secure Aggregation in Federated Learning—0
Citadel: Protecting Data Privacy and Model Confidentiality for Collaborative Learning with SGX—0
Breaking Data Silos: Towards Open and Scalable Mobility Foundation Models via Generative Continual Learning—0
A Novel Privacy-Preserved Recommender System Framework based on Federated Learning—0
Advancing Deep Learning through Probability Engineering: A Pragmatic Paradigm for Modern AI—0
An Empirical Study of Federated Learning on IoT-Edge Devices: Resource Allocation and Heterogeneity—0
Addressing Skewed Heterogeneity via Federated Prototype Rectification with Personalization—0
Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directions—0
Brave: Byzantine-Resilient and Privacy-Preserving Peer-to-Peer Federated Learning—0
Brain Tumor Detection in MRI Based on Federated Learning with YOLOv11—0
An Empirical Study of Efficiency and Privacy of Federated Learning Algorithms—0
BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning—0
Brain Storm Optimization Based Swarm Learning for Diabetic Retinopathy Image Classification—0
A Comprehensive Empirical Study of Bugs in Open-Source Federated Learning Frameworks—0
Addressing modern and practical challenges in machine learning: A survey of online federated and transfer learning—0
Abnormal Client Behavior Detection in Federated Learning—0
Communication-Efficient Federated Learning with Sketching—0
Communication-Efficient Federated Learning over Wireless Channels via Gradient Sketching—0
Communication-Efficient Framework for Distributed Image Semantic Wireless Transmission—0
CommunityAI: Towards Community-based Federated Learning—0
Brain MRI Screening Tool with Federated Learning—0
An Empirical Evaluation of Federated Contextual Bandit Algorithms—0
Achieving Personalized Federated Learning with Sparse Local Models—0
BPFISH: Blockchain and Privacy-preserving FL Inspired Smart Healthcare—0
Boosting with Multiple Sources—0
An Empirical Analysis of Federated Learning Models Subject to Label-Flipping Adversarial Attack—0
Communication-Efficient Federated Learning for Neural Machine Translation—0
Boosting the Performance of Decentralized Federated Learning via Catalyst Acceleration—0
Adaptive Gradient Clipping for Robust Federated Learning—0
Addressing Heterogeneity in Federated Load Forecasting with Personalization Layers—0
Boosting multi-demographic federated learning for chest x-ray analysis using general-purpose self-supervised representations—0
Boosting Federated Learning Convergence with Prototype Regularization—0
An Efficient Virtual Data Generation Method for Reducing Communication in Federated Learning—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