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 18011850 of 6771 papers

TitleStatusHype
Advances and Open Challenges in Federated Foundation Models0
A Comparative Study of Sampling Methods with Cross-Validation in the FedHome Framework0
Celtibero: Robust Layered Aggregation for Federated Learning0
An Operator Splitting View of Federated Learning0
CELLM: An Efficient Communication in Large Language Models Training for Federated Learning0
Anonymizing Data for Privacy-Preserving Federated Learning0
Advances and Challenges in Meta-Learning: A Technical Review0
CELEST: Federated Learning for Globally Coordinated Threat Detection0
EcoLearn: Optimizing the Carbon Footprint of Federated Learning0
An On-Device Federated Learning Approach for Cooperative Model Update between Edge Devices0
CDKT-FL: Cross-Device Knowledge Transfer using Proxy Dataset in Federated Learning0
Advancements of federated learning towards privacy preservation: from federated learning to split learning0
A Comparative Study of Federated Learning Models for COVID-19 Detection0
Accelerated Convergence of Stochastic Heavy Ball Method under Anisotropic Gradient Noise0
Random Client Selection on Contrastive Federated Learning for Tabular Data0
CDFL: Efficient Federated Human Activity Recognition using Contrastive Learning and Deep Clustering0
CD2-pFed: Cyclic Distillation-Guided Channel Decoupling for Model Personalization in Federated Learning0
CD^2-pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learning0
CCVA-FL: Cross-Client Variations Adaptive Federated Learning for Medical Imaging0
Anomaly Detection via Federated Learning0
Advancements in Federated Learning: Models, Methods, and Privacy0
CC-FedAvg: Computationally Customized Federated Averaging0
CATFL: Certificateless Authentication-based Trustworthy Federated Learning for 6G Semantic Communications0
CatFedAvg: Optimising Communication-efficiency and Classification Accuracy in Federated Learning0
Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach0
Advanced Relay-Based Collaborative Framework for Optimizing Synchronization in Split Federated Learning over Wireless Networks0
A Comparative Evaluation of FedAvg and Per-FedAvg Algorithms for Dirichlet Distributed Heterogeneous Data0
Caring Without Sharing: A Federated Learning Crowdsensing Framework for Diversifying Representation of Cities0
Anomalous Client Detection in Federated Learning0
CAPT: Class-Aware Prompt Tuning for Federated Long-Tailed Learning with Vision-Language Model0
AnoFel: Supporting Anonymity for Privacy-Preserving Federated Learning0
Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detection0
Capitalization Normalization for Language Modeling with an Accurate and Efficient Hierarchical RNN Model0
FLBench: A Benchmark Suite for Federated Learning0
Can You Really Backdoor Federated Learning?0
An Investigation towards Differentially Private Sequence Tagging in a Federated Framework0
How global observation works in Federated Learning: Integrating vertical training into Horizontal Federated Learning0
A cautionary tale on the cost-effectiveness of collaborative AI in real-world medical applications0
Can We Trust the Similarity Measurement in Federated Learning?0
Can We Theoretically Quantify the Impacts of Local Updates on the Generalization Performance of Federated Learning?0
An Interpretable Federated Learning-based Network Intrusion Detection Framework0
Can Public Large Language Models Help Private Cross-device Federated Learning?0
AdRo-FL: Informed and Secure Client Selection for Federated Learning in the Presence of Adversarial Aggregator0
Canoe : A System for Collaborative Learning for Neural Nets0
CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression0
An Intelligent Native Network Slicing Security Architecture Empowered by Federated Learning0
A(DP)^2SGD: Asynchronous Decentralized Parallel Stochastic Gradient Descent with Differential Privacy0
Can Fair Federated Learning reduce the need for Personalisation?0
Can Decentralized Learning be more robust than Federated Learning?0
An Intelligent Mechanism for Monitoring and Detecting Intrusions in IoT Devices0
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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