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 58015850 of 6771 papers

TitleStatusHype
CSAFL: A Clustered Semi-Asynchronous Federated Learning Framework0
Federated Learning for Internet of Things: A Comprehensive Survey0
FedCom: A Byzantine-Robust Local Model Aggregation Rule Using Data Commitment for Federated Learning0
On the Importance of Trust in Next-Generation Networked CPS Systems: An AI Perspective0
Federated Learning for Malware Detection in IoT DevicesCode1
Personalized Semi-Supervised Federated Learning for Human Activity Recognition0
A Method to Reveal Speaker Identity in Distributed ASR Training, and How to Counter ItCode0
D-Cliques: Compensating for Data Heterogeneity with Topology in Decentralized Federated Learning0
Efficient Ring-topology Decentralized Federated Learning with Deep Generative Models for Industrial Artificial Intelligent0
FedSAE: A Novel Self-Adaptive Federated Learning Framework in Heterogeneous Systems0
Decentralized Federated Learning for UAV Networks: Architecture, Challenges, and Opportunities0
See through Gradients: Image Batch Recovery via GradInversion0
The Role of Cross-Silo Federated Learning in Facilitating Data Sharing in the Agri-Food Sector0
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural NetworksCode1
Federated Learning-based Active Authentication on Mobile Devices0
Federated Generalized Face Presentation Attack Detection0
Resource Rationing for Wireless Federated Learning: Concept, Benefits, and Challenges0
Towards Causal Federated Learning For Enhanced Robustness and Privacy0
BROADCAST: Reducing Both Stochastic and Compression Noise to Robustify Communication-Efficient Federated LearningCode0
Sample-based and Feature-based Federated Learning for Unconstrained and Constrained Nonconvex Optimization via Mini-batch SSCACode0
Practical Defences Against Model Inversion Attacks for Split Neural NetworksCode1
FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Search0
Bayesian Variational Federated Learning and Unlearning in Decentralized Networks0
Joint Optimization of Communications and Federated Learning Over the Air0
FedFace: Collaborative Learning of Face Recognition Model0
Empowering Prosumer Communities in Smart Grid with Wireless Communications and Federated Edge Learning0
On-device Federated Learning with Flower0
Communication-Efficient Agnostic Federated Averaging0
Accelerated Gradient Tracking over Time-varying Graphs for Decentralized Optimization0
Distributed Learning in Wireless Networks: Recent Progress and Future Challenges0
FedPandemic: A Cross-Device Federated Learning Approach Towards Elementary Prognosis of Diseases During a Pandemic0
A Federated Learning Framework for Non-Intrusive Load Monitoring0
Knowledge Distillation For Wireless Edge LearningCode0
Federated Double Deep Q-learning for Joint Delay and Energy Minimization in IoT networks0
Fast-adapting and Privacy-preserving Federated Recommender System0
Decentralized and Model-Free Federated Learning: Consensus-Based Distillation in Function Space0
On the Convergence Time of Federated Learning Over Wireless Networks Under Imperfect CSI0
PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNNCode1
Federated Few-Shot Learning with Adversarial Learning0
Delay Analysis of Wireless Federated Learning Based on Saddle Point Approximation and Large Deviation Theory0
Federated Learning: A Signal Processing Perspective0
1-Bit Compressive Sensing for Efficient Federated Learning Over the Air0
Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing0
User profile-driven large-scale multi-agent learning from demonstration in federated human-robot collaborative environments0
Model-Contrastive Federated LearningCode1
Federated Learning with Taskonomy for Non-IID DataCode1
Privacy and Trust Redefined in Federated Machine LearningCode0
Hierarchical Federated Learning with Quantization: Convergence Analysis and System Design0
Prior-Independent Auctions for the Demand Side of Federated Learning0
FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning0
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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