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 44014450 of 6771 papers

TitleStatusHype
Differentially Private Vertical Federated ClusteringCode1
Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement LearningCode1
DeFL: Decentralized Weight Aggregation for Cross-silo Federated Learning0
Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updates0
FedVARP: Tackling the Variance Due to Partial Client Participation in Federated Learning0
ReFRS: Resource-efficient Federated Recommender System for Dynamic and Diversified User Preferences0
Federated Learning for IoUT: Concepts, Applications, Challenges and Opportunities0
AI Approaches in Processing and Using Data in Personalized Medicine0
CFLIT: Coexisting Federated Learning and Information Transfer0
Reconciling Security and Communication Efficiency in Federated LearningCode1
Collaboration in Participant-Centric Federated Learning: A Game-Theoretical Perspective0
Technical Report: Assisting Backdoor Federated Learning with Whole Population Knowledge Alignment0
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
Spatial-Temporal Federated Learning for Lifelong Person Re-identification on Distributed EdgesCode1
Handling Data Heterogeneity in Federated Learning via Knowledge Distillation and FusionCode0
Federated Semi-Supervised Domain Adaptation via Knowledge Transfer0
Federated Learning on Adaptively Weighted Nodes by Bilevel Optimization0
FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data0
UniFed: All-In-One Federated Learning Platform to Unify Open-Source FrameworksCode1
FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning0
Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directions0
Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMMCode0
Reducing Training Time in Cross-Silo Federated Learning using Multigraph TopologyCode0
Slimmable Quantum Federated Learning0
Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Design0
On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarios0
FedNet2Net: Saving Communication and Computations in Federated Learning with Model Growing0
FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious ClientsCode1
Over-the-Air Federated Edge Learning with Hierarchical Clustering0
SphereFed: Hyperspherical Federated Learning0
FedX: Unsupervised Federated Learning with Cross Knowledge DistillationCode1
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
Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications0
FLAIR: Federated Learning Annotated Image RepositoryCode1
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
Federated Self-Supervised Learning in Heterogeneous Settings: Limits of a Baseline Approach on HAR0
Federated Continual Learning through distillation in pervasive computing0
Fast Composite Optimization and Statistical Recovery in Federated Learning0
Federated Learning and catastrophic forgetting in pervasive computing: demonstration in HAR domain0
Multi-Task and Transfer Learning for Federated Learning Applications0
Balancing Accuracy and Integrity for Reconfigurable Intelligent Surface-aided Over-the-Air Federated Learning0
Collaborative Learning in Kernel-based Bandits for Distributed Users0
Sotto Voce: Federated Speech Recognition with Differential Privacy Guarantees0
Suppressing Poisoning Attacks on Federated Learning for Medical ImagingCode0
Introducing Federated Learning into Internet of Things ecosystems -- preliminary considerations0
Communication-Efficient Diffusion Strategy for Performance Improvement of Federated Learning with Non-IID DataCode1
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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