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

Showing 61016150 of 6771 papers

TitleStatusHype
Efficient Wireless Federated Learning via Low-Rank Gradient FactorizationCode0
Aggregation Delayed Federated LearningCode0
A Survey on Group Fairness in Federated Learning: Challenges, Taxonomy of Solutions and Directions for Future ResearchCode0
Low-Resource Machine Translation through the Lens of Personalized Federated LearningCode0
LR-XFL: Logical Reasoning-based Explainable Federated LearningCode0
Federated LoRA with Sparse CommunicationCode0
The Impact of Adversarial Node Placement in Decentralized Federated Learning NetworksCode0
Personalization Improves Privacy-Accuracy Tradeoffs in Federated LearningCode0
FLStore: Efficient Federated Learning Storage for non-training workloadsCode0
Decentralized Hyper-Gradient Computation over Time-Varying Directed NetworksCode0
FedVal: Different good or different bad in federated learningCode0
LOKI: Large-scale Data Reconstruction Attack against Federated Learning through Model ManipulationCode0
M22: A Communication-Efficient Algorithm for Federated Learning Inspired by Rate-DistortionCode0
SRATTA : Sample Re-ATTribution Attack of Secure Aggregation in Federated LearningCode0
FedVision: An Online Visual Object Detection Platform Powered by Federated LearningCode0
Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated LearningCode0
Personalized Federated Deep Learning for Pain Estimation From Face ImagesCode0
Secure Aggregation is Not Private Against Membership Inference AttacksCode0
Lessons from Generalization Error Analysis of Federated Learning: You May Communicate Less Often!Code0
The Impact of Data Distribution on Fairness and Robustness in Federated LearningCode0
Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-Ray DataCode0
FACT: Federated Adversarial Cross TrainingCode0
Understanding Training-Data Leakage from Gradients in Neural Networks for Image ClassificationCode0
FAA-CLIP: Federated Adversarial Adaptation of CLIPCode0
FeDXL: Provable Federated Learning for Deep X-Risk OptimizationCode0
Exploring Selective Layer Fine-Tuning in Federated LearningCode0
Federated Learning with Unreliable Clients: Performance Analysis and Mechanism DesignCode0
Stabilized Proximal-Point Methods for Federated OptimizationCode0
Aggregating Intrinsic Information to Enhance BCI Performance through Federated LearningCode0
Personalized Federated Learning: A Unified Framework and Universal Optimization TechniquesCode0
Federated Learning with Uncertainty-Based Client Clustering for Fleet-Wide Fault DiagnosisCode0
FEDZIP: A Compression Framework for Communication-Efficient Federated LearningCode0
Cross-Silo Heterogeneous Model Federated Multitask LearningCode0
Defending Against Diverse Attacks in Federated Learning Through Consensus-Based Bi-Level OptimizationCode0
Anomaly Detection through Unsupervised Federated LearningCode0
Using Autoencoders on Differentially Private Federated Learning GANsCode0
Federated Learning with Reservoir State Analysis for Time Series Anomaly DetectionCode0
Aligning Logits Generatively for Principled Black-Box Knowledge DistillationCode0
Federated Learning with Reduced Information Leakage and ComputationCode0
Federated Learning with Only Positive LabelsCode0
Marginal and training-conditional guarantees in one-shot federated conformal predictionCode0
MARINA: Faster Non-Convex Distributed Learning with CompressionCode0
A generic framework for privacy preserving deep learningCode0
The More is not the Merrier: Investigating the Effect of Client Size on Federated LearningCode0
Masked Autoencoders are Efficient Continual Federated LearnersCode0
Federated Learning with Non-IID DataCode0
Masked Autoencoders are Parameter-Efficient Federated Continual LearnersCode0
A Framework for testing Federated Learning algorithms using an edge-like environmentCode0
FHBench: Towards Efficient and Personalized Federated Learning for Multimodal HealthcareCode0
An Interpretable Client Decision Tree Aggregation process for Federated LearningCode0
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Benchmark Results

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