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 28012850 of 6771 papers

TitleStatusHype
Data Similarity-Based One-Shot Clustering for Multi-Task Hierarchical Federated Learning0
Federated Learning in Wireless Networks via Over-the-Air Computations0
Federated Learning is Better with Non-Homomorphic Encryption0
Federated Learning: Issues in Medical Application0
Communication-Efficient Federated Learning via Optimal Client Sampling0
Federated Learning Meets Fluid Antenna: Towards Robust and Scalable Edge Intelligence0
Artificial Intelligence in Pediatric Echocardiography: Exploring Challenges, Opportunities, and Clinical Applications with Explainable AI and Federated Learning0
Federated Learning Method for Preserving Privacy in Face Recognition System0
Federated Learning -- Methods, Applications and beyond0
Federated learning model for predicting major postoperative complications0
Federated Learning with Dynamic Client Arrival and Departure: Convergence and Rapid Adaptation via Initial Model Construction0
Federated Learning of a Mixture of Global and Local Models0
DBFed: Debiasing Federated Learning Framework based on Domain-Independent0
FedControl: When Control Theory Meets Federated Learning0
Communication Efficient Federated Learning over Multiple Access Channels0
Communication-Efficient Federated Group Distributionally Robust Optimization0
FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants0
D-Cliques: Compensating for Data Heterogeneity with Topology in Decentralized Federated Learning0
DEAL: Decremental Energy-Aware Learning in a Federated System0
A Unified Knowledge Distillation Framework for Deep Directed Graphical Models0
Federated Learning of Molecular Properties with Graph Neural Networks in a Heterogeneous Setting0
Federated Learning of N-gram Language Models0
Federated Learning Of Out-Of-Vocabulary Words0
Federated Learning of Shareable Bases for Personalization-Friendly Image Classification0
DearFSAC: An Approach to Optimizing Unreliable Federated Learning via Deep Reinforcement Learning0
Federated Learning of User Authentication Models0
Federated Learning of User Verification Models Without Sharing Embeddings0
Federated Learning on Adaptively Weighted Nodes by Bilevel Optimization0
Federated Learning on Edge Sensing Devices: A Review0
Artificial Intelligence for Personalized Prediction of Alzheimer's Disease Progression: A Survey of Methods, Data Challenges, and Future Directions0
Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning0
AI Models Close to your Chest: Robust Federated Learning Strategies for Multi-site CT0
Federated Learning on Non-IID Data: A Survey0
A Federated F-score Based Ensemble Model for Automatic Rule Extraction0
Federated Learning on Non-iid Data via Local and Global Distillation0
Federated Learning with Dynamic Transformer for Text to Speech0
Decentralised Resource Sharing in TinyML: Wireless Bilayer Gossip Parallel SGD for Collaborative Learning0
Federated Learning on Riemannian Manifolds0
FedComm: Federated Learning as a Medium for Covert Communication0
Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehicles0
Federated Learning in Genetics: Extended Analysis of Accuracy, Performance and Privacy Trade-offs0
Artificial Intelligence Driven UAV-NOMA-MEC in Next Generation Wireless Networks0
Federated Learning: Opportunities and Challenges0
Federated Learning Optimization: A Comparative Study of Data and Model Exchange Strategies in Dynamic Networks0
Federated Learning: Organizational Opportunities, Challenges, and Adoption Strategies0
Federated learning-outcome prediction with multi-layer privacy protection0
FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models0
FedCom: A Byzantine-Robust Local Model Aggregation Rule Using Data Commitment for Federated Learning0
Federated Learning over Coupled Graphs0
Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregation0
Show:102550
← PrevPage 57 of 136Next →

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