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 101150 of 6771 papers

TitleStatusHype
Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order OptimizationCode1
A Blockchain-based Decentralized Federated Learning Framework with Committee ConsensusCode1
C2A: Client-Customized Adaptation for Parameter-Efficient Federated LearningCode1
CoCoFL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and QuantizationCode1
Collaborative Fairness in Federated LearningCode1
Combating Exacerbated Heterogeneity for Robust Models in Federated LearningCode1
Adaptive and Parallel Split Federated Learning in Vehicular Edge ComputingCode1
Communication Efficient and Provable Federated UnlearningCode1
Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance ReductionCode1
Communication-Efficient Federated Learning with Accelerated Client GradientCode1
Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoTCode1
EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated LearningCode1
Communication-Efficient Heterogeneous Federated Learning with Generalized Heavy-Ball MomentumCode1
Communication-Efficient Learning of Deep Networks from Decentralized DataCode1
ByzFL: Research Framework for Robust Federated LearningCode1
CAFE: Catastrophic Data Leakage in Vertical Federated LearningCode1
Continual Local Training for Better Initialization of Federated ModelsCode1
Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy GuaranteesCode1
Cross-Silo Prototypical Calibration for Federated Learning with Non-IID DataCode1
DAGER: Exact Gradient Inversion for Large Language ModelsCode1
Data-Free Knowledge Distillation for Heterogeneous Federated LearningCode1
TabLeak: Tabular Data Leakage in Federated LearningCode1
DBA: Distributed Backdoor Attacks against Federated LearningCode1
Decentralized Federated Learning: Balancing Communication and Computing CostsCode1
Accumulative Poisoning Attacks on Real-time DataCode1
Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech RecognitionCode1
Byzantine-Robust Learning on Heterogeneous Datasets via BucketingCode1
ACCO: Accumulate While You Communicate for Communication-Overlapped Sharded LLM TrainingCode1
Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive CollaborationCode1
Masked Jigsaw Puzzle: A Versatile Position Embedding for Vision TransformersCode1
Acceleration of Federated Learning with Alleviated Forgetting in Local TrainingCode1
Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT ImagingCode1
Byzantine-Robust Decentralized Learning via ClippedGossipCode1
Can Foundation Models Help Us Achieve Perfect Secrecy?Code1
Beyond Traditional Threats: A Persistent Backdoor Attack on Federated LearningCode1
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
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated LearningCode1
Bayesian Framework for Gradient LeakageCode1
Backdoor Attacks on Federated Learning with Lottery Ticket HypothesisCode1
FedAdapter: Efficient Federated Learning for Modern NLPCode1
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine LearningCode1
Analysis and Evaluation of Synchronous and Asynchronous FLchainCode1
CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated LearningCode1
An In-Depth Evaluation of Federated Learning on Biomedical Natural Language ProcessingCode1
Asynchronous Federated Learning for Edge-assisted Vehicular NetworksCode1
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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