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 25012550 of 6771 papers

TitleStatusHype
Byzantine-Robust Decentralized Federated Learning0
Recent Advances in Federated Learning Driven Large Language Models: A Survey on Architecture, Performance, and Security0
Heterogeneous Federated Learning with Convolutional and Spiking Neural Networks0
Federated Learning with Flexible Architectures0
EncCluster: Scalable Functional Encryption in Federated Learning through Weight Clustering and Probabilistic Filters0
FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature AugmentationCode0
A deep cut into Split Federated Self-supervised LearningCode0
Regularizing and Aggregating Clients with Class Distribution for Personalized Federated LearningCode0
A Federated Online Restless Bandit Framework for Cooperative Resource Allocation0
Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data0
IMFL-AIGC: Incentive Mechanism Design for Federated Learning Empowered by Artificial Intelligence Generated Content0
Minimizing Energy Costs in Deep Learning Model Training: The Gaussian Sampling Approach0
Federated Nonparametric Hypothesis Testing with Differential Privacy Constraints: Optimal Rates and Adaptive Tests0
Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learning0
Decentralized Personalized Federated Learning0
Fed-Sophia: A Communication-Efficient Second-Order Federated Learning Algorithm0
Optimisation of federated learning settings under statistical heterogeneity variations0
Optimal Federated Learning for Nonparametric Regression with Heterogeneous Distributed Differential Privacy Constraints0
Federated learning in food research0
PTF-FSR: A Parameter Transmission-Free Federated Sequential Recommender SystemCode0
When Swarm Learning meets energy series data: A decentralized collaborative learning design based on blockchain0
Federated LoRA with Sparse CommunicationCode0
Federated Representation Learning in the Under-Parameterized RegimeCode0
R-CONV: An Analytical Approach for Efficient Data Reconstruction via Convolutional GradientsCode0
1-D CNN-Based Online Signature Verification with Federated Learning0
FedStaleWeight: Buffered Asynchronous Federated Learning with Fair Aggregation via Staleness ReweightingCode0
Buffered Asynchronous Secure Aggregation for Cross-Device Federated Learning0
Noise-Aware Algorithm for Heterogeneous Differentially Private Federated LearningCode0
Towards Federated Domain Unlearning: Verification Methodologies and Challenges0
A Comparative Study of Sampling Methods with Cross-Validation in the FedHome Framework0
Optimal Rates for O(1)-Smooth DP-SCO with a Single Epoch and Large Batches0
Federated Class-Incremental Learning with Hierarchical Generative Prototypes0
Parameterizing Federated Continual Learning for Reproducible Research0
FedDr+: Stabilizing Dot-regression with Global Feature Distillation for Federated Learning0
Mixed-Precision Federated Learning via Multi-Precision Over-The-Air Aggregation0
One-Shot Federated Learning with Bayesian Pseudocoresets0
Efficient Data Distribution Estimation for Accelerated Federated Learning0
No Vandalism: Privacy-Preserving and Byzantine-Robust Federated Learning0
Federated Learning-based Collaborative Wideband Spectrum Sensing and Scheduling for UAVs in UTM Systems0
Asynchronous Multi-Server Federated Learning for Geo-Distributed Clients0
Accelerating Heterogeneous Federated Learning with Closed-form Classifiers0
Asynchronous Byzantine Federated Learning0
FedAdOb: Privacy-Preserving Federated Deep Learning with Adaptive Obfuscation0
Cohort Squeeze: Beyond a Single Communication Round per Cohort in Cross-Device Federated Learning0
A Novel Defense Against Poisoning Attacks on Federated Learning: LayerCAM Augmented with AutoencoderCode0
Local Methods with Adaptivity via Scaling0
Federated Model Heterogeneous Matryoshka Representation Learning0
SpaFL: Communication-Efficient Federated Learning with Sparse Models and Low computational OverheadCode0
FedAST: Federated Asynchronous Simultaneous Training0
Selective Knowledge Sharing for Personalized Federated Learning Under Capacity Heterogeneity0
Show:102550
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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