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 61016150 of 6771 papers

TitleStatusHype
Edge-assisted Democratized Learning Towards Federated Analytics0
Fast-Convergent Federated Learning with Adaptive Weighting0
Inverting Gradients - How easy is it to break privacy in federated learning?Code1
Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach0
LocKedge: Low-Complexity Cyberattack Detection in IoT Edge Computing0
Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Management0
Advancements of federated learning towards privacy preservation: from federated learning to split learning0
Optimizing Resource-Efficiency for Federated Edge Intelligence in IoT Networks0
Toward Multiple Federated Learning Services Resource Sharing in Mobile Edge NetworksCode1
Wyner-Ziv Estimators for Distributed Mean Estimation with Side Information and OptimizationCode0
Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan0
Federated learning with class imbalance reduction0
LINDT: Tackling Negative Federated Learning with Local Adaptation0
Improving Federated Relational Data Modeling via Basis Alignment and Weight Penalty0
A decentralized aggregation mechanism for training deep learning models using smart contract system for bank loan prediction0
Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design ApproachCode1
A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated LearningCode1
FedEval: A Holistic Evaluation Framework for Federated Learning0
FLaaS: Federated Learning as a Service0
Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networks0
Federated Composite OptimizationCode1
Private Wireless Federated Learning with Anonymous Over-the-Air Computation0
Stochastic Client Selection for Federated Learning with Volatile Clients0
Budgeted Online Selection of Candidate IoT Clients to Participate in Federated Learning0
2CP: Decentralized Protocols to Transparently Evaluate Contributivity in Blockchain Federated Learning Environments0
Dynamic backdoor attacks against federated learning0
FedRec: Federated Learning of Universal Receivers over Fading ChannelsCode0
CatFedAvg: Optimising Communication-efficiency and Classification Accuracy in Federated Learning0
Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations0
Federated Multi-Mini-Batch: An Efficient Training Approach to Federated Learning in Non-IID Environments0
Hybrid Federated and Centralized Learning0
Fed-Focal Loss for imbalanced data classification in Federated Learning0
Coded Computing for Low-Latency Federated Learning over Wireless Edge Networks0
Heterogeneous Data-Aware Federated Learning0
A Novel Privacy-Preserved Recommender System Framework based on Federated Learning0
Federated Learning via Intelligent Reflecting Surface0
Privacy Preservation in Federated Learning: An insightful survey from the GDPR Perspective0
Compression Boosts Differentially Private Federated Learning0
Mitigating Leakage in Federated Learning with Trusted Hardware0
Distributed Learning with Low Communication Cost via Gradient Boosting Untrained Neural Network0
Interpretable collaborative data analysis on distributed data0
Adaptive Federated Dropout: Improving Communication Efficiency and Generalization for Federated Learning0
FDNAS: Improving Data Privacy and Model Diversity in AutoML0
Federated Crowdsensing: Framework and Challenges0
FedSL: Federated Split Learning on Distributed Sequential Data in Recurrent Neural NetworksCode0
Resource-Constrained Federated Learning with Heterogeneous Labels and Models0
Collaborative City Digital Twin For Covid-19 Pandemic: A Federated Learning Solution0
Federated Knowledge DistillationCode1
BaFFLe: Backdoor detection via Feedback-based Federated Learning0
Local SGD: Unified Theory and New Efficient Methods0
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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