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 151200 of 6771 papers

TitleStatusHype
DAGER: Exact Gradient Inversion for Large Language ModelsCode1
Bias Propagation in Federated LearningCode1
Benchmarking Differential Privacy and Federated Learning for BERT ModelsCode1
Benchmarking Algorithms for Federated Domain GeneralizationCode1
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of ModelsCode1
Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated Graph Neural NetworkCode1
ARFED: Attack-Resistant Federated averaging based on outlier eliminationCode1
Beyond Traditional Threats: A Persistent Backdoor Attack on Federated LearningCode1
Masked Jigsaw Puzzle: A Versatile Position Embedding for Vision TransformersCode1
Adaptive and Parallel Split Federated Learning in Vehicular Edge ComputingCode1
Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy GuaranteesCode1
Byzantine-Robust Learning on Heterogeneous Datasets via BucketingCode1
CAFE: Catastrophic Data Leakage in Vertical Federated LearningCode1
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated LearningCode1
CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated LearningCode1
Can Textual Gradient Work in Federated Learning?Code1
CEFHRI: A Communication Efficient Federated Learning Framework for Recognizing Industrial Human-Robot InteractionCode1
CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian SamplingCode1
Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated LearningCode1
Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept DriftCode1
Bayesian Framework for Gradient LeakageCode1
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated LearningCode1
CoCoFL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and QuantizationCode1
CoDeC: Communication-Efficient Decentralized Continual LearningCode1
Backdoor Attacks on Federated Learning with Lottery Ticket HypothesisCode1
Adapt to Adaptation: Learning Personalization for Cross-Silo Federated LearningCode1
Acceleration of Federated Learning with Alleviated Forgetting in Local TrainingCode1
Communication-Efficient Diffusion Strategy for Performance Improvement of Federated Learning with Non-IID DataCode1
Adaptive Test-Time Personalization for Federated LearningCode1
Communication Efficient Federated Learning for Multilingual Neural Machine Translation with AdapterCode1
ACCO: Accumulate While You Communicate for Communication-Overlapped Sharded LLM TrainingCode1
Communication-Efficient Federated Learning Based on Explanation-Guided Pruning for Remote Sensing Image ClassificationCode1
Accumulative Poisoning Attacks on Real-time DataCode1
Adaptive Federated OptimizationCode1
Communication-Efficient Heterogeneous Federated Learning with Generalized Heavy-Ball MomentumCode1
Communication-Efficient Learning of Deep Networks from Decentralized DataCode1
FedAdapter: Efficient Federated Learning for Modern NLPCode1
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine LearningCode1
Continual Local Training for Better Initialization of Federated ModelsCode1
Analysis and Evaluation of Synchronous and Asynchronous FLchainCode1
Data-Free Knowledge Distillation for Heterogeneous Federated LearningCode1
TabLeak: Tabular Data Leakage in Federated LearningCode1
Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated LearningCode1
A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data SourcesCode1
ALI-DPFL: Differentially Private Federated Learning with Adaptive Local IterationsCode1
A Better Alternative to Error Feedback for Communication-Efficient Distributed LearningCode1
Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and ChallengesCode1
An In-Depth Evaluation of Federated Learning on Biomedical Natural Language ProcessingCode1
DeceFL: A Principled Decentralized Federated Learning FrameworkCode1
Attack of the Tails: Yes, You Really Can Backdoor Federated LearningCode1
Show:102550
← PrevPage 4 of 136Next →

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