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 52515300 of 6771 papers

TitleStatusHype
FedFly: Towards Migration in Edge-based Distributed Federated LearningCode1
Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Training0
Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories0
To Talk or to Work: Delay Efficient Federated Learning over Mobile Edge Devices0
Resource-Efficient Federated LearningCode1
FedFm: Towards a Robust Federated Learning Approach For Fault Mitigation at the Edge Nodes0
Robust Federated Learning via Over-The-Air Computation0
Implicit Model Specialization through DAG-based Decentralized Federated LearningCode1
Efficient passive membership inference attack in federated learningCode1
DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning0
Wireless Federated Learning over MIMO Networks: Joint Device Scheduling and Beamforming Design0
Revealing and Protecting Labels in Distributed TrainingCode0
Dynamic Differential-Privacy Preserving SGD0
Federated Semi-Supervised Learning with Class Distribution Mismatch0
Improving Fairness via Federated LearningCode1
Gradient Inversion with Generative Image PriorCode1
Towards Model Agnostic Federated Learning Using Knowledge Distillation0
Federated Learning with Heterogeneous Differential Privacy0
Communication-Efficient ADMM-based Federated LearningCode0
Differentially Private Federated Bayesian Optimization with Distributed Exploration0
FedPrune: Towards Inclusive Federated Learning0
Over-the-Air Aggregation for Federated Learning: Waveform Superposition and Prototype Validation0
Spatio-Temporal Federated Learning for Massive Wireless Edge Networks0
What Do We Mean by Generalization in Federated Learning?0
CAFE: Catastrophic Data Leakage in Vertical Federated LearningCode1
Ensemble Federated Adversarial Training with Non-IID data0
Fault-Tolerant Federated Reinforcement Learning with Theoretical GuaranteeCode1
Semi-Supervised Federated Learning with non-IID Data: Algorithm and System Design0
DPCOVID: Privacy-Preserving Federated Covid-19 Detection0
FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client PerspectiveCode1
MarS-FL: Enabling Competitors to Collaborate in Federated Learning0
Qu-ANTI-zation: Exploiting Quantization Artifacts for Achieving Adversarial OutcomesCode1
Optimal Model Averaging: Towards Personalized Collaborative Learning0
Federated Test-Time Adaptive Face Presentation Attack Detection with Dual-Phase Privacy Preservation0
Optimization-Based GenQSGD for Federated Edge Learning0
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified ModelsCode1
Federated Multiple Label Hashing (FedMLH): Communication Efficient Federated Learning on Extreme Classification Tasks0
Game of Gradients: Mitigating Irrelevant Clients in Federated LearningCode0
MANDERA: Malicious Node Detection in Federated Learning via Ranking0
PRECAD: Privacy-Preserving and Robust Federated Learning via Crypto-Aided Differential Privacy0
WebFed: Cross-platform Federated Learning Framework Based on Web Browser with Local Differential Privacy0
Federated Learning over Wireless IoT Networks with Optimized Communication and Resources0
Tackling the Local Bias in Federated Graph Learning0
Federated Unlearning via Class-Discriminative PruningCode0
DistFL: Distribution-aware Federated Learning for Mobile ScenariosCode1
Bristle: Decentralized Federated Learning in Byzantine, Non-i.i.d. Environments0
Boosting Resource-Constrained Federated Learning Systems with Guessed Updates0
FedGEMS: Federated Learning of Larger Server Models via Selective Knowledge Fusion0
PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotion0
SecureBoost+: Large Scale and High-Performance Vertical Federated Gradient Boosting Decision Tree0
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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