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 3301–3350 of 6771 papers

TitleStatusHype
Federated Fine-Tuning of Foundation Models via Probabilistic Masking—0
CommunityAI: Towards Community-based Federated Learning—0
On the Effect of Defections in Federated Learning and How to Prevent Them—0
Asynchronous Wireless Federated Learning with Probabilistic Client Selection—0
Contrastive encoder pre-training-based clustered federated learning for heterogeneous data—0
MIA-BAD: An Approach for Enhancing Membership Inference Attack and its Mitigation with Federated LearningCode0
Communication Efficiency Optimization of Federated Learning for Computing and Network Convergence of 6G Networks—0
Scheduling and Communication Schemes for Decentralized Federated Learning—0
Where to Begin? From Random to Foundation Model Instructed Initialization in Federated Learning for Medical Image Segmentation—0
Using Decentralized Aggregation for Federated Learning with Differential Privacy—0
QuickDrop: Efficient Federated Unlearning by Integrated Dataset Distillation—0
Evaluating Multi-Global Server Architecture for Federated Learning—0
Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs—0
OFDMA-F^2L: Federated Learning With Flexible Aggregation Over an OFDMA Air Interface—0
ARIA: On the Interaction Between Architectures, Initialization and Aggregation Methods for Federated Visual ClassificationCode0
Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networks—0
FAMAC: A Federated Assisted Modified Actor-Critic Framework for Secured Energy Saving in 5G and Beyond Networks—0
Prototype of deployment of Federated Learning with IoT devices—0
Enhancing Intrusion Detection In Internet Of Vehicles Through Federated Learning—0
Brain MRI Screening Tool with Federated Learning—0
A Blockchain Solution for Collaborative Machine Learning over IoT—0
Federated Learning for Short Text Clustering—0
Federated Learning Assisted Distributed Energy Optimization—0
AdapterFL: Adaptive Heterogeneous Federated Learning for Resource-constrained Mobile Computing Systems—0
OASIS: Offsetting Active Reconstruction Attacks in Federated Learning—0
Have Your Cake and Eat It Too: Toward Efficient and Accurate Split Federated Learning—0
FedFN: Feature Normalization for Alleviating Data Heterogeneity Problem in Federated Learning—0
A Joint Gradient and Loss Based Clustered Federated Learning Design—0
AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems—0
MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation—0
SecureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning—0
Privacy-Preserving Load Forecasting via Personalized Model Obfuscation—0
Federated Learning via Consensus Mechanism on Heterogeneous Data: A New Perspective on ConvergenceCode0
Attacks on fairness in Federated LearningCode0
FedDRO: Federated Compositional Optimization for Distributionally Robust Learning—0
FedCPC: An Effective Federated Contrastive Learning Method for Privacy Preserving Early-Stage Alzheimer's Speech Detection—0
FBChain: A Blockchain-based Federated Learning Model with Efficiency and Secure Communication—0
Multi-Session Budget Optimization for Forward Auction-based Federated Learning—0
Energizing Federated Learning via Filter-Aware Attention—0
Leveraging Function Space Aggregation for Federated Learning at Scale—0
Identifying the Truth of Global Model: A Generic Solution to Defend Against Byzantine and Backdoor Attacks in Federated Learning (full version)—0
Exploring Machine Learning Models for Federated Learning: A Review of Approaches, Performance, and Limitations—0
A Novel Neural Network-Based Federated Learning System for Imbalanced and Non-IID Data—0
UFPS: A unified framework for partially-annotated federated segmentation in heterogeneous data distributionCode0
Contribution Evaluation in Federated Learning: Examining Current Approaches—0
Straggler-resilient Federated Learning: Tackling Computation Heterogeneity with Layer-wise Partial Model Training in Mobile Edge Network—0
FedDiff: Diffusion Model Driven Federated Learning for Multi-Modal and Multi-Clients—0
Federated Learning for Sparse Principal Component Analysis—0
Scalable Federated Learning for Clients with Different Input Image Sizes and Numbers of Output Categories—0
FedCode: Communication-Efficient Federated Learning via Transferring Codebooks—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