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 22012250 of 6771 papers

TitleStatusHype
Pencil: Private and Extensible Collaborative Learning without the Non-Colluding AssumptionCode1
Federated Transfer Learning with Differential Privacy0
Feature Aggregation with Latent Generative Replay for Federated Continual Learning of Socially Appropriate Robot Behaviours0
Federated Learning based on Pruning and RecoveryCode0
FedQNN: Federated Learning using Quantum Neural Networks0
FAGH: Accelerating Federated Learning with Approximated Global Hessian0
Enhancing IoT Security Against DDoS Attacks through Federated Learning0
Defense via Behavior Attestation against Attacks in Connected and Automated Vehicles based Federated Learning Systems0
Fairness-Aware Multi-Server Federated Learning Task Delegation over Wireless Networks0
Empowering Healthcare through Privacy-Preserving MRI Analysis0
FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models0
Explainable Machine Learning-Based Security and Privacy Protection Framework for Internet of Medical Things Systems0
Learning from straggler clients in federated learning0
Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domains0
DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning0
Metadata-Driven Federated Learning of Connectional Brain Templates in Non-IID Multi-Domain Scenarios0
Federated Knowledge Graph Unlearning via Diffusion Model0
MGIC: A Multi-Label Gradient Inversion Attack based on Canny Edge Detection on Federated Learning0
Decoupled Federated Learning on Long-Tailed and Non-IID data with Feature Statistics0
AFGI: Towards Accurate and Fast-convergent Gradient Inversion Attack in Federated Learning0
Efficient Language Model Architectures for Differentially Private Federated Learning0
Federated Learning of Socially Appropriate Agent Behaviours in Simulated Home EnvironmentsCode0
UAV-Enabled Asynchronous Federated Learning0
DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning0
Don't Forget What I did?: Assessing Client Contributions in Federated Learning0
Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains0
Adaptive Federated Learning Over the Air0
DrJAX: Scalable and Differentiable MapReduce Primitives in JAX0
Analysis of Total Variation Minimization for Clustered Federated Learning0
Fake or Compromised? Making Sense of Malicious Clients in Federated Learning0
FewFedPIT: Towards Privacy-preserving and Few-shot Federated Instruction Tuning0
Fluent: Round-efficient Secure Aggregation for Private Federated Learning0
Towards Efficient Replay in Federated Incremental Learning0
RIS-empowered Topology Control for Distributed Learning in Urban Air Mobility0
FedFMS: Exploring Federated Foundation Models for Medical Image SegmentationCode2
Federated Learning Method for Preserving Privacy in Face Recognition System0
Boosting Fairness and Robustness in Over-the-Air Federated Learning0
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression0
HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated LearningCode0
FL-GUARD: A Holistic Framework for Run-Time Detection and Recovery of Negative Federated Learning0
On-demand Quantization for Green Federated Generative Diffusion in Mobile Edge Networks0
Architectural Blueprint For Heterogeneity-Resilient Federated Learning0
FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering0
Many-Objective Multi-Solution Transport0
OCD-FL: A Novel Communication-Efficient Peer Selection-based Decentralized Federated Learning0
SPEAR:Exact Gradient Inversion of Batches in Federated Learning0
Do You Trust Your Model? Emerging Malware Threats in the Deep Learning Ecosystem0
Decoupled Vertical Federated Learning for Practical Training on Vertically Partitioned Data0
Enhancing Security in Federated Learning through Adaptive Consensus-Based Model Update Validation0
Leveraging Federated Learning for Automatic Detection of Clopidogrel Treatment Failures0
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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