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 10011050 of 6771 papers

TitleStatusHype
Analysis of Privacy Leakage in Federated Large Language ModelsCode0
Genetic CFL: Optimization of Hyper-Parameters in Clustered Federated LearningCode0
Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine LearningCode0
Federating Dynamic Models using Early-Exit Architectures for Automatic Speech Recognition on Heterogeneous ClientsCode0
GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph DataCode0
FedExP: Speeding Up Federated Averaging via ExtrapolationCode0
Federated User Preference Modeling for Privacy-Preserving Cross-Domain RecommendationCode0
Federated Unlearning Made Practical: Seamless Integration via Negated Pseudo-GradientsCode0
A Novel Defense Against Poisoning Attacks on Federated Learning: LayerCAM Augmented with AutoencoderCode0
Federated Unlearning via Class-Discriminative PruningCode0
Federated Visual Classification with Real-World Data DistributionCode0
Federated Two Stage Decoupling With Adaptive Personalization LayersCode0
Federated Survival ForestsCode0
Masked Random Noise for Communication Efficient Federated LearningCode0
Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate GradientsCode0
FedNS: A Fast Sketching Newton-Type Algorithm for Federated LearningCode0
Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things ApplicationCode0
Graph Federated Learning for CIoT Devices in Smart Home ApplicationsCode0
Federated singular value decomposition for high dimensional dataCode0
An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal EffectsCode0
Federated Representation Learning in the Under-Parameterized RegimeCode0
Lottery Aware Sparsity Hunting: Enabling Federated Learning on Resource-Limited EdgeCode0
Federated Over-Air Subspace Tracking from Incomplete and Corrupted DataCode0
Federated Optimization for Heterogeneous NetworksCode0
Federated Prediction-Powered Inference from Decentralized DataCode0
Achieving Distributive Justice in Federated Learning via Uncertainty QuantificationCode0
FedPCL-CDR: A Federated Prototype-based Contrastive Learning Framework for Privacy-Preserving Cross-domain RecommendationCode0
Federated Neural Radiance FieldsCode0
BERT WEAVER: Using WEight AVERaging to enable lifelong learning for transformer-based models in biomedical semantic search enginesCode0
Federated Noisy Client LearningCode0
Federated Spectral Graph Transformers Meet Neural Ordinary Differential Equations for Non-IID GraphsCode0
Federated Multi-Task LearningCode0
Federated Multi-Task Learning on Non-IID Data Silos: An Experimental StudyCode0
Federated Multimodal Learning with Dual Adapters and Selective Pruning for Communication and Computational EfficiencyCode0
Benchmarking Federated Learning for Semantic Datasets: Federated Scene Graph GenerationCode0
Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer InterfacesCode0
Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated LearningCode0
Achieving Byzantine-Resilient Federated Learning via Layer-Adaptive Sparsified Model AggregationCode0
Federated Multi-armed Bandits with PersonalizationCode0
Federated LoRA with Sparse CommunicationCode0
Federated Low-Rank Adaptation for Foundation Models: A SurveyCode0
Federated Learning with Unreliable Clients: Performance Analysis and Mechanism DesignCode0
BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoTCode0
Lessons from Generalization Error Analysis of Federated Learning: You May Communicate Less Often!Code0
Federated Machine Learning: Concept and ApplicationsCode0
FedBAT: Communication-Efficient Federated Learning via Learnable BinarizationCode0
Bayesian Robust Aggregation for Federated LearningCode0
Federated Learning with Reservoir State Analysis for Time Series Anomaly DetectionCode0
Federated Learning with Uncertainty-Based Client Clustering for Fleet-Wide Fault DiagnosisCode0
Federated Nearest Neighbor Classification with a Colony of Fruit-Flies: With SupplementCode0
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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