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 52015250 of 6771 papers

TitleStatusHype
Privacy-preserving Federated Learning for Residential Short Term Load Forecasting0
Differentially Private Federated Learning on Heterogeneous DataCode1
A Parameter Aggregation Strategy on Personalized Federated Learning0
Learning Tokenization in Private Federated Learning with Sub-Word Model Sampling0
FedParsing: a Semi-Supervised Federated Learning Model on Semantic Parsing0
HADFL: Heterogeneity-aware Decentralized Federated Learning Framework0
FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated LearningCode0
Wyner-Ziv Gradient Compression for Federated Learning0
FedCostWAvg: A new averaging for better Federated Learning0
Federated Learning for Smart Healthcare: A Survey0
On-Demand Unlabeled Personalized Federated Learning0
DNN gradient lossless compression: Can GenNorm be the answer?Code0
Federated Learning for Internet of Things: Applications, Challenges, and Opportunities0
On the Tradeoff between Energy, Precision, and Accuracy in Federated Quantized Neural Networks0
Power Allocation for Wireless Federated Learning using Graph Neural NetworksCode1
Federated Learning with Hyperparameter-based Clustering for Electrical Load Forecasting0
Eluding Secure Aggregation in Federated Learning via Model InconsistencyCode1
Attentive Federated Learning for Concept Drift in Distributed 5G Edge NetworksCode0
Towards Privacy-Preserving Affect Recognition: A Two-Level Deep Learning Architecture0
Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges0
An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning0
STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural NetworksCode0
Hierarchical Bayesian Bandits0
FedGreen: Federated Learning with Fine-Grained Gradient Compression for Green Mobile Edge Computing0
Fairness, Integrity, and Privacy in a Scalable Blockchain-based Federated Learning System0
Linear Speedup in Personalized Collaborative LearningCode0
DACFL: Dynamic Average Consensus Based Federated Learning in Decentralized Topology0
Data privacy protection in microscopic image analysis for material data mining0
Unified Group Fairness on Federated Learning0
The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning0
DP-REC: Private & Communication-Efficient Federated Learning0
Papaya: Practical, Private, and Scalable Federated Learning0
Bayesian Framework for Gradient LeakageCode1
ARFED: Attack-Resistant Federated averaging based on outlier eliminationCode1
Federated Learning Based on Dynamic RegularizationCode1
DQRE-SCnet: A novel hybrid approach for selecting users in Federated Learning with Deep-Q-Reinforcement Learning based on Spectral Clustering0
Privacy attacks for automatic speech recognition acoustic models in a federated learning framework0
Data Selection for Efficient Model Update in Federated Learning0
DVFL: A Vertical Federated Learning Method for Dynamic Data0
Sharp Bounds for Federated Averaging (Local SGD) and Continuous PerspectiveCode0
FedLess: Secure and Scalable Federated Learning Using Serverless ComputingCode1
Parameterized Knowledge Transfer for Personalized Federated LearningCode1
A Personalized Federated Learning Algorithm: an Application in Anomaly Detection0
A Cyber Threat Intelligence Sharing Scheme based on Federated Learning for Network Intrusion Detection0
Federated Expectation Maximization with heterogeneity mitigation and variance reduction0
FedSim: Similarity guided model aggregation for Federated LearningCode1
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning0
Towards Fairness-Aware Federated Learning0
Practical and Light-weight Secure Aggregation for Federated Submodel Learning0
FedGraph: Federated Graph Learning with Intelligent Sampling0
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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