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 54515500 of 6771 papers

TitleStatusHype
Training Fair Models in Federated Learning without Data Privacy Infringement0
Source Inference Attacks in Federated LearningCode1
Byzantine-robust Federated Learning through Collaborative Malicious Gradient FilteringCode1
Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News RecommendationCode1
Federated Ensemble Model-based Reinforcement Learning in Edge Computing0
Cost-Effective Federated Learning in Mobile Edge Networks0
Critical Learning Periods in Federated Learning0
FedTriNet: A Pseudo Labeling Method with Three Players for Federated Semi-supervised Learning0
On the Initial Behavior Monitoring Issues in Federated Learning0
Utility Fairness for the Differentially Private Federated Learning0
Toward Communication Efficient Adaptive Gradient Method0
Multimodal Federated Learning on IoT Data0
A distillation-based approach integrating continual learning and federated learning for pervasive services0
An Experimental Study of Class Imbalance in Federated Learning0
Towards Efficient Synchronous Federated Training: A Survey on System Optimization StrategiesCode0
Iterated Vector Fields and Conservatism, with Applications to Federated Learning0
FedZKT: Zero-Shot Knowledge Transfer towards Resource-Constrained Federated Learning with Heterogeneous On-Device Models0
Dubhe: Towards Data Unbiasedness with Homomorphic Encryption in Federated Learning Client Selection0
Federated Learning Beyond the Star: Local D2D Model Consensus with Global Cluster SamplingCode0
Generation of Synthetic Electronic Health Records Using a Federated GAN0
On Second-order Optimization Methods for Federated Learning0
Byzantine-Robust Federated Learning via Credibility Assessment on Non-IID Data0
F3: Fair and Federated Face Attribute Classification with Heterogeneous DataCode0
GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated LearningCode0
Ground-Assisted Federated Learning in LEO Satellite Constellations0
A Synergetic Attack against Neural Network Classifiers combining Backdoor and Adversarial Examples0
Statistical Estimation and Inference via Local SGD in Federated Learning0
FLASHE: Additively Symmetric Homomorphic Encryption for Cross-Silo Federated LearningCode1
Asynchronous Federated Learning for Sensor Data with Concept Drift0
Federated Learning: Issues in Medical Application0
Unit-Modulus Wireless Federated Learning Via Penalty Alternating Minimization0
GRP-FED: Addressing Client Imbalance in Federated Learning via Global-Regularized Personalization0
FedKD: Communication Efficient Federated Learning via Knowledge Distillation0
Private Multi-Task Learning: Formulation and Applications to Federated LearningCode0
Canoe : A System for Collaborative Learning for Neural Nets0
Enabling SQL-based Training Data Debugging for Federated Learning0
Federated Reinforcement Learning: Techniques, Applications, and Open Challenges0
PIVODL: Privacy-preserving vertical federated learning over distributed labels0
Federated Learning for Open Banking0
Data-Free Evaluation of User Contributions in Federated Learning0
Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health0
Federated Learning for UAV Swarms Under Class Imbalance and Power Consumption Constraints0
Anarchic Federated Learning0
Federated Learning Meets Fairness and Differential PrivacyCode0
Federated Multi-Task Learning under a Mixture of DistributionsCode1
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated LearningCode1
Personalised Federated Learning: A Combinational Approach0
Flexible Clustered Federated Learning for Client-Level Data Distribution ShiftCode1
SemiFed: Semi-supervised Federated Learning with Consistency and Pseudo-Labeling0
Accelerating Federated Learning with a Global Biased OptimiserCode0
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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