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 62516300 of 6771 papers

TitleStatusHype
Federated Learning of Medical Concepts Embedding using BEHRTCode0
Exact Penalty Method for Federated LearningCode0
FLrce: Resource-Efficient Federated Learning with Early-Stopping StrategyCode0
Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated LearningCode0
FLsim: A Modular and Library-Agnostic Simulation Framework for Federated LearningCode0
Personalized Federated Learning with Multiple Known ClustersCode0
Personalized Federated Learning with Server-Side InformationCode0
Randomized Quantization is All You Need for Differential Privacy in Federated LearningCode0
Stochastic Unrolled Federated LearningCode0
Federated Learning of Large Models at the Edge via Principal Sub-Model TrainingCode0
Mitigating Group Bias in Federated Learning: Beyond Local FairnessCode0
Federated Causal Inference from Observational DataCode0
A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural NetworksCode0
Ranking-Based At-Risk Student Prediction Using Federated Learning and Differential FeaturesCode0
The Skellam Mechanism for Differentially Private Federated LearningCode0
Securing Distributed SGD against Gradient Leakage ThreatsCode0
FLuID: Mitigating Stragglers in Federated Learning using Invariant DropoutCode0
Federated Learning Meets Fairness and Differential PrivacyCode0
Federated Learning in Unreliable and Resource-Constrained Cellular Wireless NetworksCode0
Rate-Distortion Theoretic Bounds on Generalization Error for Distributed LearningCode0
Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic DataCode0
Straggler-Resilient Personalized Federated LearningCode0
Personalized Multi-tier Federated LearningCode0
Mitigating Sybils in Federated Learning PoisoningCode0
Federated Learning in Chemical Engineering: A Tutorial on a Framework for Privacy-Preserving Collaboration Across Distributed Data SourcesCode0
The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoostCode0
Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor SegmentationCode0
FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and DetectionCode0
Personalized Online Federated Learning with Multiple KernelsCode0
Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model UpdatesCode0
Federated Learning in ASR: Not as Easy as You ThinkCode0
Forgetting Any Data at Any Time: A Theoretically Certified Unlearning Framework for Vertical Federated LearningCode0
Personalized Privacy-Preserving Framework for Cross-Silo Federated LearningCode0
A Field Guide to Federated OptimizationCode0
Decentralized Personalized Federated Learning based on a Conditional Sparse-to-Sparser SchemeCode0
Evaluating Federated Kolmogorov-Arnold Networks on Non-IID DataCode0
An Improved Algorithm for Clustered Federated LearningCode0
Estimation of Microphone Clusters in Acoustic Sensor Networks using Unsupervised Federated LearningCode0
FPPL: An Efficient and Non-IID Robust Federated Continual Learning FrameworkCode0
Thinking Two Moves Ahead: Anticipating Other Users Improves Backdoor Attacks in Federated LearningCode0
FRAIN to Train: A Fast-and-Reliable Solution for Decentralized Federated LearningCode0
Decentralized Event-Triggered Federated Learning with Heterogeneous Communication ThresholdsCode0
Framework for Co-distillation Driven Federated Learning to Address Class Imbalance in HealthcareCode0
R-CONV: An Analytical Approach for Efficient Data Reconstruction via Convolutional GradientsCode0
FRAug: Tackling Federated Learning with Non-IID Features via Representation AugmentationCode0
A New Perspective to Boost Performance Fairness for Medical Federated LearningCode0
Reactive Orchestration for Hierarchical Federated Learning Under a Communication Cost BudgetCode0
Realistic Urban Traffic Generator using Decentralized Federated Learning for the SUMO simulatorCode0
FedER: Federated Learning through Experience Replay and Privacy-Preserving Data SynthesisCode0
Federated Learning Hyper-Parameter Tuning from a System PerspectiveCode0
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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