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 53015350 of 6771 papers

TitleStatusHype
Distributed Reinforcement Learning for Privacy-Preserving Dynamic Edge Caching0
FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computing0
Layer-wise Adaptive Model Aggregation for Scalable Federated Learning0
A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and ComparisonCode1
FedHe: Heterogeneous Models and Communication-Efficient Federated LearningCode0
TESSERACT: Gradient Flip Score to Secure Federated Learning Against Model Poisoning Attacks0
UniFed: A Unified Framework for Federated Learning on Non-IID Image Features0
Tackling Dynamics in Federated Incremental Learning with Variational Embedding Rehearsal0
User-Centric Federated Learning0
BEV-SGD: Best Effort Voting SGD for Analog Aggregation Based Federated Learning against Byzantine Attackers0
Towards Federated Bayesian Network Structure Learning with Continuous OptimizationCode1
Sim-to-Real Transfer in Multi-agent Reinforcement Networking for Federated Edge Computing0
Towards General Deep Leakage in Federated LearningCode0
Semi-asynchronous Hierarchical Federated Learning for Cooperative Intelligent Transportation Systems0
A Federated Approach to Predict Emojis in Hindi Tweets0
Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensing0
Adapt to Adaptation: Learning Personalization for Cross-Silo Federated LearningCode1
FedSLD: Federated Learning with Shared Label Distribution for Medical Image Classification0
FedSEAL: Semi-Supervised Federated Learning with Self-Ensemble Learning and Negative Learning0
Evaluation of Hyperparameter-Optimization Approaches in an Industrial Federated Learning System0
FedMe: Federated Learning via Model Exchange0
EdgeML: Towards Network-Accelerated Federated Learning over Wireless Edge0
Leveraging Spatial and Temporal Correlations in Sparsified Mean Estimation0
Federated Learning for COVID-19 Detection with Generative Adversarial Networks in Edge Cloud ComputingCode1
Federated learning and next generation wireless communications: A survey on bidirectional relationship0
FedSpeech: Federated Text-to-Speech with Continual Learning0
Distribution-Free Federated Learning with Conformal Predictions0
Modeling and Analysis of Intermittent Federated Learning Over Cellular-Connected UAV Networks0
WAFFLE: Weighted Averaging for Personalized Federated Learning0
Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated LearningCode1
Infinitely Divisible Noise in the Low Privacy RegimeCode0
Communication-Efficient Online Federated Learning Framework for Nonlinear RegressionCode0
Application of Homomorphic Encryption in Medical Imaging0
Federated Phish Bowl: LSTM-Based Decentralized Phishing Email Detection0
Deep Federated Learning for Autonomous DrivingCode1
Partial Variable Training for Efficient On-Device Federated Learning0
Homogeneous Learning: Self-Attention Decentralized Deep LearningCode0
An Information-Theoretic Analysis of The Cost of Decentralization for Learning and Inference Under Privacy Constraints0
The Skellam Mechanism for Differentially Private Federated LearningCode0
ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive TrainingCode1
Gradual Federated Learning with Simulated Annealing0
FRL: Federated Rank Learning0
Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions0
Exploring Heterogeneous Characteristics of Layers in ASR Models for More Efficient Training0
Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees0
Complex-valued Federated Learning with Differential Privacy and MRI Applications0
Enabling On-Device Training of Speech Recognition Models with Federated Dropout0
Neural Tangent Kernel Empowered Federated Learning0
Towards Federated Learning-Enabled Visible Light Communication in 6G Systems0
Federated Learning from Small DatasetsCode1
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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