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 36513700 of 6771 papers

TitleStatusHype
Over-the-Air Computation Aided Federated Learning with the Aggregation of Normalized Gradient0
Over-the-Air Decentralized Federated Learning0
Over-the-Air Federated Averaging with Limited Power and Privacy Budgets0
Over-the-Air Federated Edge Learning with Hierarchical Clustering0
Over-the-Air Federated Learning and Optimization0
Over-the-Air Federated Learning In Broadband Communication0
Over-the-Air Federated Learning in Cell-Free MIMO with Long-term Power Constraint0
Over-the-Air Federated Learning in Satellite systems0
Over-the-Air Federated Learning Over MIMO Channels: A Sparse-Coded Multiplexing Approach0
Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMO0
Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions0
Over-The-Air Federated Learning under Byzantine Attacks0
Over-the-Air Federated Learning via Weighted Aggregation0
Over-the-Air Federated Learning with Retransmissions (Extended Version)0
Over-the-Air Federated Learning with Compressed Sensing: Is Sparsification Necessary?0
Over-the-Air Federated Learning with Joint Adaptive Computation and Power Control0
Over-the-Air Federated Learning with Phase Noise: Analysis and Countermeasures0
Over-the-Air Federated Learning with Privacy Protection via Correlated Additive Perturbations0
Over-the-Air Federated Multi-Task Learning Over MIMO Multiple Access Channels0
P3LS: Partial Least Squares under Privacy Preservation0
P4L: Privacy Preserving Peer-to-Peer Learning for Infrastructureless Setups0
PackVFL: Efficient HE Packing for Vertical Federated Learning0
PAFedFV: Personalized and Asynchronous Federated Learning for Finger Vein Recognition0
PAGE: Equilibrate Personalization and Generalization in Federated Learning0
Papaya: Federated Learning, but Fully Decentralized0
Papaya: Practical, Private, and Scalable Federated Learning0
Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator0
Parallel Restarted SPIDER -- Communication Efficient Distributed Nonconvex Optimization with Optimal Computation Complexity0
Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks0
Parameter-Efficient Transfer Learning under Federated Learning for Automatic Speech Recognition0
Parameterizing Federated Continual Learning for Reproducible Research0
Gradient Correction in Federated Learning with Adaptive Optimization0
Parametric Feature Transfer: One-shot Federated Learning with Foundation Models0
PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization0
Partial Federated Learning0
Partial Knowledge Distillation for Alleviating the Inherent Inter-Class Discrepancy in Federated Learning0
Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity0
Partial Model Averaging in Federated Learning: Performance Guarantees and Benefits0
Partial Variable Training for Efficient On-Device Federated Learning0
Particle Swarm Optimized Federated Learning For Industrial IoT and Smart City Services0
Partitioned Variational Inference: A unified framework encompassing federated and continual learning0
Partner in Crime: Boosting Targeted Poisoning Attacks against Federated Learning0
Past, Present, Future: A Comprehensive Exploration of AI Use Cases in the UMBRELLA IoT Testbed0
PatternGPT :A Pattern-Driven Framework for Large Language Model Text Generation0
PBM-VFL: Vertical Federated Learning with Feature and Sample Privacy0
PCDP-SGD: Improving the Convergence of Differentially Private SGD via Projection in Advance0
PDFed: Privacy-Preserving and Decentralized Asynchronous Federated Learning for Diffusion Models0
Peer-to-Peer Deep Learning for Beyond-5G IoT0
Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognition0
Peer-to-peer Federated Learning on Graphs0
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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