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 48014850 of 6771 papers

TitleStatusHype
Towards Energy-Aware Federated Learning on Battery-Powered ClientsCode0
PEPPER: Empowering User-Centric Recommender Systems over Gossip Learning0
Application of federated learning in manufacturing0
Learning-Based Client Selection for Federated Learning Services Over Wireless Networks with Constrained Monetary Budgets0
Low-Latency Cooperative Spectrum Sensing via Truncated Vertical Federated Learning0
Distributed Contrastive Learning for Medical Image Segmentation0
Federated Adversarial Learning: A Framework with Convergence Analysis0
Terahertz-Band Channel and Beam Split Estimation via Array Perturbation Model0
Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions0
Embedding Alignment for Unsupervised Federated Learning via Smart Data Exchange0
FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning0
ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity0
A New Implementation of Federated Learning for Privacy and Security Enhancement0
How Much Privacy Does Federated Learning with Secure Aggregation Guarantee?0
Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning0
DeFL: Decentralized Weight Aggregation for Cross-silo Federated Learning0
Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updates0
Federated Learning for IoUT: Concepts, Applications, Challenges and Opportunities0
ReFRS: Resource-efficient Federated Recommender System for Dynamic and Diversified User Preferences0
FedVARP: Tackling the Variance Due to Partial Client Participation in Federated Learning0
CFLIT: Coexisting Federated Learning and Information Transfer0
AI Approaches in Processing and Using Data in Personalized Medicine0
Technical Report: Assisting Backdoor Federated Learning with Whole Population Knowledge Alignment0
Collaboration in Participant-Centric Federated Learning: A Game-Theoretical Perspective0
BPFISH: Blockchain and Privacy-preserving FL Inspired Smart Healthcare0
Privacy Against Inference Attacks in Vertical Federated Learning0
Mitigating the Performance Sacrifice in DP-Satisfied Federated Settings through Graph Contrastive Learning0
Handling Data Heterogeneity in Federated Learning via Knowledge Distillation and FusionCode0
Federated Learning on Adaptively Weighted Nodes by Bilevel Optimization0
Federated Semi-Supervised Domain Adaptation via Knowledge Transfer0
FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data0
Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directions0
Reducing Training Time in Cross-Silo Federated Learning using Multigraph TopologyCode0
FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning0
Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMMCode0
Slimmable Quantum Federated Learning0
Over-the-Air Federated Edge Learning with Hierarchical Clustering0
Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Design0
On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarios0
SphereFed: Hyperspherical Federated Learning0
FedNet2Net: Saving Communication and Computations in Federated Learning with Model Growing0
Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyond0
Training Large-Vocabulary Neural Language Models by Private Federated Learning for Resource-Constrained Devices0
Federated Learning for Non-IID Data via Client Variance Reduction and Adaptive Server Update0
Study of the performance and scalability of federated learning for medical imaging with intermittent clients0
Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications0
Federated Learning and catastrophic forgetting in pervasive computing: demonstration in HAR domain0
Federated Continual Learning through distillation in pervasive computing0
Balancing Accuracy and Integrity for Reconfigurable Intelligent Surface-aided Over-the-Air Federated Learning0
Multi-Task and Transfer Learning for Federated Learning Applications0
Show:102550
← PrevPage 97 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