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 66516700 of 6771 papers

TitleStatusHype
Privacy Preserving Stochastic Channel-Based Federated Learning with Neural Network Pruning0
Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy ConstraintsCode0
Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large-Scale Health System Intelligence0
SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead0
Privacy-preserving Federated Brain Tumour Segmentation0
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization0
Federated User Representation Learning0
Improving Federated Learning Personalization via Model Agnostic Meta LearningCode0
Active Federated Learning0
Federated Learning in Mobile Edge Networks: A Comprehensive Survey0
Model Pruning Enables Efficient Federated Learning on Edge DevicesCode0
Low Rank Training of Deep Neural Networks for Emerging Memory Technology0
On Federated Learning of Deep Networks from Non-IID Data: Parameter Divergence and the Effects of Hyperparametric Methods0
Optimal query complexity for private sequential learning against eavesdropping0
Towards Federated Graph Learning for Collaborative Financial Crimes Detection0
Detailed comparison of communication efficiency of split learning and federated learning0
Measure Contribution of Participants in Federated Learning0
A Joint Learning and Communications Framework for Federated Learning over Wireless NetworksCode0
BAFFLE : Blockchain Based Aggregator Free Federated LearningCode0
Differentially Private Meta-Learning0
Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging0
HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for ElectroencephalographyCode0
Gradient Descent with Compressed Iterates0
First Analysis of Local GD on Heterogeneous Data0
Hierarchical Federated Learning Across Heterogeneous Cellular Networks0
Energy Demand Prediction with Federated Learning for Electric Vehicle Networks0
Rewarding High-Quality Data via Influence Functions0
An End-to-End Encrypted Neural Network for Gradient Updates Transmission in Federated Learning0
Federated Learning: Challenges, Methods, and Future DirectionsCode0
Towards Effective Device-Aware Federated Learning0
Federated Learning with Additional Mechanisms on Clients to Reduce Communication CostsCode0
Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges0
A Federated Learning Approach for Mobile Packet Classification0
Federated Learning over Wireless Fading Channels0
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and ProtectionCode0
FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare0
The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoostCode0
Privacy-Preserving Classification with Secret Vector MachinesCode0
Wireless Federated Distillation for Distributed Edge Learning with Heterogeneous Data0
Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning ApplicationsCode0
Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices0
Active Learning Solution on Distributed Edge Computing0
Privacy Preserving QoE Modeling using Collaborative Learning0
Scalable and Differentially Private Distributed Aggregation in the Shuffled Model0
Robust Federated Learning in a Heterogeneous Environment0
Variational Federated Multi-Task Learning0
Secure Federated Matrix Factorization0
Federated Learning for Emoji Prediction in a Mobile Keyboard0
Federated AI lets a team imagine together: Federated Learning of GANs0
Adaptive Gradient-Based Meta-Learning MethodsCode0
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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