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 27012750 of 6771 papers

TitleStatusHype
Communication Efficient Federated Learning via Ordered ADMM in a Fully Decentralized Setting0
Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges0
Federated Learning for Healthcare Informatics0
Federated Learning for ICD Classification with Lightweight Models and Pretrained Embeddings0
Federated Learning for Industrial Internet of Things in Future Industries0
Federated Learning for Inference at Anytime and Anywhere0
Federated Learning for Internet of Things: Applications, Challenges, and Opportunities0
Federated Learning for Internet of Things: A Comprehensive Survey0
FedCross: Intertemporal Federated Learning Under Evolutionary Games0
Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions0
Federated Learning for Intrusion Detection in IoT Security: A Hybrid Ensemble Approach0
Federated Learning for IoUT: Concepts, Applications, Challenges and Opportunities0
D2p-fed:Differentially Private Federated Learning with Efficient Communication0
Federated Learning for Large-Scale Scene Modeling with Neural Radiance Fields0
Federated Learning for Localization: A Privacy-Preserving Crowdsourcing Method0
FedCRL: Personalized Federated Learning with Contrastive Shared Representations for Label Heterogeneity in Non-IID Data0
Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions0
Federated Learning for Medical Image Classification: A Comprehensive Benchmark0
Federated Learning for Medical Image Analysis: A Survey0
Federated Learning for Metaverse: A Survey0
DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning0
Adaptive Decentralized Federated Learning in Energy and Latency Constrained Wireless Networks0
Communication Efficient Federated Learning for Generalized Linear Bandits0
A Safe Genetic Algorithm Approach for Energy Efficient Federated Learning in Wireless Communication Networks0
Data-Aware Gradient Compression for FL in Communication-Constrained Mobile Computing0
Federated Learning for Non-IID Data via Unified Feature Learning and Optimization Objective Alignment0
Fed-Credit: Robust Federated Learning with Credibility Management0
Federated Learning for Open Banking0
Federated Learning for Physical Layer Design0
Federated Learning for Predicting Mild Cognitive Impairment to Dementia Conversion0
Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applications0
Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey0
Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health0
DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning0
FedCPC: An Effective Federated Contrastive Learning Method for Privacy Preserving Early-Stage Alzheimer's Speech Detection0
FedCostWAvg: A new averaging for better Federated Learning0
Communication-Efficient Federated Learning for Neural Machine Translation0
Federated learning for secure development of AI models for Parkinson's disease detection using speech from different languages0
DASHA: Distributed Nonconvex Optimization with Communication Compression, Optimal Oracle Complexity, and No Client Synchronization0
Federated Learning for Short-term Residential Load Forecasting0
Federated Learning for Short Text Clustering0
Federated Learning for Smart Grid: A Survey on Applications and Potential Vulnerabilities0
A Safe Deep Reinforcement Learning Approach for Energy Efficient Federated Learning in Wireless Communication Networks0
Federated Learning for Sparse Principal Component Analysis0
Federated Learning for Spoken Language Understanding0
Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case0
Federated Learning for Task and Resource Allocation in Wireless High Altitude Balloon Networks0
Federated Learning for the Classification of Tumor Infiltrating Lymphocytes0
Federated Multi-Task Learning for THz Wideband Channel and DoA Estimation0
Active 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