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 55015550 of 6771 papers

TitleStatusHype
Fairness, Integrity, and Privacy in a Scalable Blockchain-based Federated Learning System0
FedGreen: Federated Learning with Fine-Grained Gradient Compression for Green Mobile Edge Computing0
DACFL: Dynamic Average Consensus Based Federated Learning in Decentralized Topology0
Linear Speedup in Personalized Collaborative LearningCode0
Data privacy protection in microscopic image analysis for material data mining0
Unified Group Fairness on Federated Learning0
DP-REC: Private & Communication-Efficient Federated Learning0
The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning0
Papaya: Practical, Private, and Scalable Federated Learning0
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
DVFL: A Vertical Federated Learning Method for Dynamic Data0
Sharp Bounds for Federated Averaging (Local SGD) and Continuous PerspectiveCode0
Data Selection for Efficient Model Update in Federated Learning0
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
FedGraph: Federated Graph Learning with Intelligent Sampling0
Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Training0
Towards Fairness-Aware Federated Learning0
Practical and Light-weight Secure Aggregation for Federated Submodel Learning0
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning0
Robust Federated Learning via Over-The-Air Computation0
FedFm: Towards a Robust Federated Learning Approach For Fault Mitigation at the Edge Nodes0
Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories0
To Talk or to Work: Delay Efficient Federated Learning over Mobile Edge Devices0
DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning0
Revealing and Protecting Labels in Distributed TrainingCode0
Wireless Federated Learning over MIMO Networks: Joint Device Scheduling and Beamforming Design0
Dynamic Differential-Privacy Preserving SGD0
Federated Semi-Supervised Learning with Class Distribution Mismatch0
Towards Model Agnostic Federated Learning Using Knowledge Distillation0
Federated Learning with Heterogeneous Differential Privacy0
Communication-Efficient ADMM-based Federated LearningCode0
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
Differentially Private Federated Bayesian Optimization with Distributed Exploration0
Semi-Supervised Federated Learning with non-IID Data: Algorithm and System Design0
MarS-FL: Enabling Competitors to Collaborate in Federated Learning0
DPCOVID: Privacy-Preserving Federated Covid-19 Detection0
Ensemble Federated Adversarial Training with Non-IID data0
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
Federated Multiple Label Hashing (FedMLH): Communication Efficient Federated Learning on Extreme Classification Tasks0
Game of Gradients: Mitigating Irrelevant Clients in Federated LearningCode0
Federated Unlearning via Class-Discriminative PruningCode0
Federated Learning over Wireless IoT Networks with Optimized Communication and Resources0
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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