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 46014650 of 6771 papers

TitleStatusHype
Rate-Distortion Theoretic Bounds on Generalization Error for Distributed LearningCode0
(Im)possibility of Collective Intelligence0
Sharper Rates and Flexible Framework for Nonconvex SGD with Client and Data SamplingCode0
Federated Adversarial Training with Transformers0
Straggler-Resilient Personalized Federated LearningCode0
Scheduling for Ground-Assisted Federated Learning in LEO Satellite Constellations0
Distributed Machine Learning in D2D-Enabled Heterogeneous Networks: Architectures, Performance, and Open Challenges0
UAV-Aided Multi-Community Federated Learning0
Towards Group Learning: Distributed Weighting of Experts0
Federated Deep Learning Meets Autonomous Vehicle Perception: Design and VerificationCode1
On the Generalization of Wasserstein Robust Federated Learning0
Completion Time Minimization of Fog-RAN-Assisted Federated Learning With Rate-Splitting Transmission0
Supernet Training for Federated Image Classification under System HeterogeneityCode0
A federated graph neural network framework for privacy-preserving personalizationCode1
Resource Allocation for Compression-aided Federated Learning with High Distortion Rate0
HEX: Human-in-the-loop Explainability via Deep Reinforcement Learning0
Federated Learning with a Sampling Algorithm under Isoperimetry0
Applied Federated Learning: Architectural Design for Robust and Efficient Learning in Privacy Aware Settings0
Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic DataCode0
Federated Learning in Satellite Constellations0
Walk for Learning: A Random Walk Approach for Federated Learning from Heterogeneous Data0
Defense Against Gradient Leakage Attacks via Learning to Obscure Data0
DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse TrainingCode1
Optimization with Access to Auxiliary InformationCode0
Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the TopCode0
Federated Learning under Distributed Concept DriftCode1
Asynchronous Hierarchical Federated Learning0
Near-Optimal Collaborative Learning in BanditsCode0
Pseudo-Data based Self-Supervised Federated Learning for Classification of Histopathological Images0
A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting0
FedHarmony: Unlearning Scanner Bias with Distributed DataCode0
VFed-SSD: Towards Practical Vertical Federated Advertising0
Secure Federated Clustering0
Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity0
FRAug: Tackling Federated Learning with Non-IID Features via Representation AugmentationCode0
FedAUXfdp: Differentially Private One-Shot Federated Distillation0
Maximizing Global Model Appeal in Federated Learning0
CalFAT: Calibrated Federated Adversarial Training with Label SkewnessCode0
Confederated Learning: Federated Learning with Decentralized Edge Servers0
FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality PredictionCode0
Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition0
Towards Communication-Learning Trade-off for Federated Learning at the Network Edge0
FedControl: When Control Theory Meets Federated Learning0
FedFormer: Contextual Federation with Attention in Reinforcement LearningCode1
Federated Semi-Supervised Learning with Prototypical NetworksCode0
FadMan: Federated Anomaly Detection across Multiple Attributed Networks0
FedAvg with Fine Tuning: Local Updates Lead to Representation Learning0
AsyncFedED: Asynchronous Federated Learning with Euclidean Distance based Adaptive Weight AggregationCode0
Can Foundation Models Help Us Achieve Perfect Secrecy?Code1
Mixed Federated Learning: Joint Decentralized and Centralized Learning0
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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