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 5601–5650 of 6771 papers

TitleStatusHype
Towards Federated Learning-Enabled Visible Light Communication in 6G Systems—0
FedTune: Automatic Tuning of Federated Learning Hyper-Parameters from System PerspectiveCode0
SSFL: Tackling Label Deficiency in Federated Learning via Personalized Self-Supervision—0
Two-Bit Aggregation for Communication Efficient and Differentially Private Federated Learning—0
Federated Learning via Plurality VoteCode0
Secure Byzantine-Robust Distributed Learning via Clustering—0
Efficient and Private Federated Learning with Partially Trainable Networks—0
Secure Aggregation for Buffered Asynchronous Federated Learning—0
A Systematic Survey of Blockchained Federated Learning—0
Federating for Learning Group Fair Models—0
FedDQ: Communication-Efficient Federated Learning with Descending Quantization—0
Distributed Learning Approaches for Automated Chest X-Ray Diagnosis—0
Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits—0
Privacy enabled Financial Text Classification using Differential Privacy and Federated Learning—0
SecFL: Confidential Federated Learning using TEEs—0
FairFed: Enabling Group Fairness in Federated Learning—0
Layer-wise and Dimension-wise Locally Adaptive Federated Learning—0
Lightweight Transformer in Federated Setting for Human Activity Recognition—0
Algorithm Fairness in AI for Medicine and Healthcare—0
Federated Learning in ASR: Not as Easy as You ThinkCode0
Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devices—0
Coding for Straggler Mitigation in Federated Learning—0
Secure Multi-Party Computation based Privacy Preserving Data Analysis in Healthcare IoT Systems—0
Iterative Sketching and its Application to Federated Learning—0
Inference-Time Personalized Federated Learning—0
LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning—0
Hybrid Local SGD for Federated Learning with Heterogeneous Communications—0
FSL: Federated Supermask Learning—0
FLBoost: On-the-Fly Fine-tuning Boosts Federated Learning via Data-free Distillation—0
FEVERLESS: Fast and Secure Vertical Federated Learning based on XGBoost for Decentralized Labels—0
FedNAS: Federated Deep Learning via Neural Architecture Search—0
FedMorph: Communication Efficient Federated Learning via Morphing Neural Network—0
On Heterogeneously Distributed Data, Sparsity Matters—0
Towards Communication-Efficient and Privacy-Preserving Federated Representation Learning—0
Federated Learning with Data-Agnostic Distribution Fusion—0
Personalized Heterogeneous Federated Learning with Gradient Similarity—0
Personalized Neural Architecture Search for Federated Learning—0
FEDERATED LEARNING FRAMEWORK BASED ON TRIMMED MEAN AGGREGATION RULES—0
Picking Daisies in Private: Federated Learning from Small Datasets—0
Positive and Unlabeled Federated Learning—0
Practical and Private Heterogeneous Federated Learning—0
Predictive Maintenance for Optical Networks in Robust Collaborative Learning—0
Federated Inference through Aligning Local Representations and Learning a Consensus Graph—0
Federated Contrastive Representation Learning with Feature Fusion and Neighborhood Matching—0
FedDrop: Trajectory-weighted Dropout for Efficient Federated Learning—0
FedDiscrete: A Secure Federated Learning Algorithm Against Weight Poisoning—0
Provable Federated Adversarial Learning via Min-max Optimization—0
FedBABU: Toward Enhanced Representation for Federated Image Classification—0
Fairness-aware Federated Learning—0
EP-GAN: Unsupervised Federated Learning with Expectation-Propagation Prior GAN—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