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 57515800 of 6771 papers

TitleStatusHype
The Federated Tumor Segmentation (FeTS) ChallengeCode1
Federated Unbiased Learning to Rank0
FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Convergence Analysis0
Separate but Together: Unsupervised Federated Learning for Speech Enhancement from Non-IID DataCode1
Federated Learning with Unreliable Clients: Performance Analysis and Mechanism DesignCode0
Latency Analysis of Consortium Blockchained Federated Learning0
Slashing Communication Traffic in Federated Learning by Transmitting Clustered Model Updates0
Loss Tolerant Federated LearningCode0
A Hybrid Architecture for Federated and Centralized Learning0
FedGL: Federated Graph Learning Framework with Global Self-Supervision0
Membership Inference Attacks on Deep Regression Models for Neuroimaging0
Federated Face Recognition0
Byzantine-Robust and Privacy-Preserving Framework for FedML0
Citadel: Protecting Data Privacy and Model Confidentiality for Collaborative Learning with SGX0
Federated Multi-View Learning for Private Medical Data Integration and Analysis0
OCTOPUS: Overcoming Performance andPrivatization Bottlenecks in Distributed Learning0
Personalized Federated Learning by Structured and Unstructured Pruning under Data HeterogeneityCode1
GRNN: Generative Regression Neural Network -- A Data Leakage Attack for Federated LearningCode1
FedProto: Federated Prototype Learning across Heterogeneous ClientsCode1
Convergence Analysis and System Design for Federated Learning over Wireless Networks0
Federated Learning with Fair AveragingCode1
End-to-End Speech Recognition from Federated Acoustic ModelsCode1
PPFL: Privacy-preserving Federated Learning with Trusted Execution EnvironmentsCode1
Privacy-Preserving Federated Learning on Partitioned Attributes0
From Distributed Machine Learning to Federated Learning: A Survey0
Cluster-driven Graph Federated Learning over Multiple Domains0
Towards On-Device Federated Learning: A Direct Acyclic Graph-based Blockchain Approach0
Confined Gradient Descent: Privacy-preserving Optimization for Federated Learning0
Towards Fair Federated Learning with Zero-Shot Data Augmentation0
Secure and Efficient Federated Learning Through Layering and Sharding Blockchain0
Simultaneous Wireless Information and Power Transfer for Federated Learning0
Continual Distributed Learning for Crisis Management0
A Graph Federated Architecture with Privacy Preserving Learning0
FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia0
Communication-Efficient Federated Learning with Dual-Side Low-Rank Compression0
Communication-Efficient and Personalized Federated Lottery Ticket Learning0
Semi-Decentralized Federated Edge Learning for Fast Convergence on Non-IID Data0
FedSup: A Communication-Efficient Federated Learning Fatigue Driving Behaviors Supervision Framework0
Wireless Federated Learning (WFL) for 6G Networks -- Part I: Research Challenges and Future Trends0
Wireless Federated Learning (WFL) for 6G Networks -- Part II: The Compute-then-Transmit NOMA Paradigm0
Robust Federated Learning by Mixture of ExpertsCode0
Blockchain based Privacy-Preserved Federated Learning for Medical Images: A Case Study of COVID-19 CT Scans0
Gradient Masked Federated Optimization0
Turning Federated Learning Systems Into Covert Channels0
A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things0
Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation0
Federated Word2Vec: Leveraging Federated Learning to Encourage Collaborative Representation Learning0
Research on Resource Allocation for Efficient Federated Learning0
Federated Learning of User Verification Models Without Sharing Embeddings0
FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing TasksCode0
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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