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 151–200 of 6771 papers

TitleStatusHype
Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling—0
Reconciling Hessian-Informed Acceleration and Scalar-Only Communication for Efficient Federated Zeroth-Order Fine-Tuning—0
Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning—0
FedRPCA: Enhancing Federated LoRA Aggregation Using Robust PCA—0
Label-shift robust federated feature screening for high-dimensional classification—0
Towards Graph-Based Privacy-Preserving Federated Learning: ModelNet -- A ResNet-based Model Classification Dataset—0
Blockchain-Enabled Privacy-Preserving Second-Order Federated Edge Learning in Personalized Healthcare—0
Federated learning framework for collaborative remaining useful life prognostics: an aircraft engine case studyCode0
Assortment of Attention Heads: Accelerating Federated PEFT with Head Pruning and Strategic Client Selection—0
The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian SketchesCode0
Robust Federated Learning against Model Perturbation in Edge Networks—0
ByzFL: Research Framework for Robust Federated LearningCode1
INSIGHT: A Survey of In-Network Systems for Intelligent, High-Efficiency AI and Topology Optimization—0
Adaptive Deadline and Batch Layered Synchronized Federated Learning—0
Personalized Subgraph Federated Learning with Differentiable Auxiliary Projections—0
Loss-Guided Model Sharing and Local Learning Correction in Decentralized Federated Learning for Crop Disease Classification—0
Measuring Participant Contributions in Decentralized Federated Learning—0
An Empirical Study of Federated Prompt Learning for Vision Language Model—0
On Global Convergence Rates for Federated Policy Gradient under Heterogeneous Environment—0
Position: Federated Foundation Language Model Post-Training Should Focus on Open-Source Models—0
Deep Modeling and Optimization of Medical Image ClassificationCode0
FSL-SAGE: Accelerating Federated Split Learning via Smashed Activation Gradient EstimationCode0
The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated LearningCode1
Federated Unsupervised Semantic Segmentation—0
Accelerated Training of Federated Learning via Second-Order Methods—0
Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats—0
CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning—0
PathFL: Multi-Alignment Federated Learning for Pathology Image SegmentationCode0
Inclusive, Differentially Private Federated Learning for Clinical Data—0
Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated LearningCode1
DP-RTFL: Differentially Private Resilient Temporal Federated Learning for Trustworthy AI in Regulated IndustriesCode0
DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models—0
Multimodal Federated Learning: A Survey through the Lens of Different FL Paradigms—0
Addressing Data Quality Decompensation in Federated Learning via Dynamic Client SelectionCode0
Unified Alignment Protocol: Making Sense of the Unlabeled Data in New Domains—0
Voronoi-grid-based Pareto Front Learning and Its Application to Collaborative Federated LearningCode0
Federated Instrumental Variable Analysis via Federated Generalized Method of Moments—0
AMSFL: Adaptive Multi-Step Federated Learning via Gradient Difference-Based Error Modeling—0
Fairness in Federated Learning: Fairness for Whom?—0
Label Leakage in Federated Inertial-based Human Activity RecognitionCode0
Privacy-Preserving Chest X-ray Report Generation via Multimodal Federated Learning with ViT and GPT-2—0
Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things—0
SEMFED: Semantic-Aware Resource-Efficient Federated Learning for Heterogeneous NLP Tasks—0
Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data—0
Federated Learning-Distillation Alternation for Resource-Constrained IoT—0
LAPA-based Dynamic Privacy Optimization for Wireless Federated Learning in Heterogeneous Environments—0
Generalized and Personalized Federated Learning with Foundation Models via Orthogonal Transformations—0
Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity—0
Exploring the Possibility of TypiClust for Low-Budget Federated Active Learning—0
Multimodal Federated Learning With Missing Modalities through Feature Imputation NetworkCode0
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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