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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2951–3000 of 6771 papers

TitleStatusHype
Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach—0
Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey—0
Blockchain-empowered Federated Learning: Benefits, Challenges, and SolutionsCode0
Federated Learning via Lattice Joint Source-Channel Coding—0
FedRDMA: Communication-Efficient Cross-Silo Federated LLM via Chunked RDMA Transmission—0
Cloud-based Federated Learning Framework for MRI Segmentation—0
Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data SilosCode0
Federated Linear Contextual Bandits with Heterogeneous Clients—0
On the Convergence of Federated Learning Algorithms without Data SimilarityCode0
RobWE: Robust Watermark Embedding for Personalized Federated Learning Model Ownership Protection—0
PrivatEyes: Appearance-based Gaze Estimation Using Federated Secure Multi-Party Computation—0
SPriFed-OMP: A Differentially Private Federated Learning Algorithm for Sparse Basis Recovery—0
Decoupled Subgraph Federated LearningCode0
Improving Group Connectivity for Generalization of Federated Deep Learning—0
CollaFuse: Navigating Limited Resources and Privacy in Collaborative Generative AICode0
Decentralised Traffic Incident Detection via Network Lasso—0
Auditable Homomorphic-based Decentralized Collaborative AI with Attribute-based Differential Privacy—0
Impact of network topology on the performance of Decentralized Federated Learning—0
Communication Efficient ConFederated Learning: An Event-Triggered SAGA Approach—0
FedUV: Uniformity and Variance for Heterogeneous Federated Learning—0
Federated Learning for Estimating Heterogeneous Treatment Effects—0
FedBRB: An Effective Solution to the Small-to-Large Scenario in Device-Heterogeneity Federated Learning—0
MIP: CLIP-based Image Reconstruction from PEFT Gradients—0
Watch Your Head: Assembling Projection Heads to Save the Reliability of Federated ModelsCode0
BlockFUL: Enabling Unlearning in Blockchained Federated Learning—0
Multiple Access in the Era of Distributed Computing and Edge Intelligence—0
FedReview: A Review Mechanism for Rejecting Poisoned Updates in Federated Learning—0
Distribution-Free Fair Federated Learning with Small Samples—0
Bayesian Neural Network For Personalized Federated Learning Parameter Selection—0
Trustworthy Personalized Bayesian Federated Learning via Posterior Fine-Tune—0
CoDream: Exchanging dreams instead of models for federated aggregation with heterogeneous models—0
ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devices—0
FedMM: Federated Multi-Modal Learning with Modality Heterogeneity in Computational Pathology—0
Chu-ko-nu: A Reliable, Efficient, and Anonymously Authentication-Enabled Realization for Multi-Round Secure Aggregation in Federated Learning—0
A Bargaining-based Approach for Feature Trading in Vertical Federated Learning—0
Convergence Analysis of Split Federated Learning on Heterogeneous Data—0
Efficient Unbiased Sparsification—0
RVE-PFL: Robust Variational Encoder-based Personalised Federated Learning against Model Inversion AttacksCode0
Federated Learning in Genetics: Extended Analysis of Accuracy, Performance and Privacy Trade-offs—0
Privacy-Enhancing Collaborative Information Sharing through Federated Learning -- A Case of the Insurance Industry—0
Federated Fairness without Access to Sensitive Groups—0
Federated Neural Graph Databases—0
Practical Insights into Knowledge Distillation for Pre-Trained Models—0
FedADMM-InSa: An Inexact and Self-Adaptive ADMM for Federated Learning—0
Stochastic Approximation Approach to Federated Machine Learning—0
Federated Multi-Task Learning on Non-IID Data Silos: An Experimental StudyCode0
Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates—0
Random Aggregate Beamforming for Over-the-Air Federated Learning in Large-Scale Networks—0
FGAD: Self-boosted Knowledge Distillation for An Effective Federated Graph Anomaly Detection Framework—0
Secure Federated Learning Across Heterogeneous Cloud and High-Performance Computing Resources -- A Case Study on Federated Fine-tuning of LLaMA 2—0
Show:102550
← PrevPage 60 of 136Next →
All datasetsCityscapes heterogeneousLandmarks-User-160kCIFAR-100 (alpha=0, 10 clients per round)CIFAR-100 (alpha=0, 20 clients per round)CIFAR-100 (alpha=0.5, 10 clients per round)CIFAR-100 (alpha=0.5, 20 clients per round)CIFAR-100 (alpha=0.5, 5 clients per round)CIFAR-100 (alpha=0, 5 clients per round)CIFAR-100 (alpha=1000, 10 clients per round)CIFAR-100 (alpha=1000, 20 clients per round)CIFAR-100 (alpha=1000, 5 clients per round)CIFAR100 (alpha=0.3, 10 clients per round)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SiloBN + ASAMmIoU49.75—Unverified
2SiloBN + SAMmIoU49.1—Unverified
3SiloBNmIoU45.96—Unverified
4FedSAM + SWAmIoU43.42—Unverified
5FedASAM + SWAmIoU43.02—Unverified
6FedAvg + SWAmIoU42.48—Unverified
7FedASAMmIoU42.27—Unverified
8FedSAMmIoU41.22—Unverified
9FedAvgmIoU38.65—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAAcc@1-1262Clients68.32—Unverified
2FedSAM + SWAAcc@1-1262Clients68.12—Unverified
3FedAvg + SWAAcc@1-1262Clients67.52—Unverified
4FedASAMAcc@1-1262Clients64.23—Unverified
5FedSAMAcc@1-1262Clients63.72—Unverified
6FedAvgAcc@1-1262Clients61.91—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.64—Unverified
2FedASAMACC@1-100Clients39.76—Unverified
3FedSAM + SWAACC@1-100Clients39.51—Unverified
4FedSAMACC@1-100Clients36.93—Unverified
5FedAvgACC@1-100Clients36.74—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients41.62—Unverified
2FedASAMACC@1-100Clients40.81—Unverified
3FedSAM + SWAACC@1-100Clients39.24—Unverified
4FedAvgACC@1-100Clients38.59—Unverified
5FedSAMACC@1-100Clients38.56—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.72—Unverified
2FedSAM + SWAACC@1-100Clients46.76—Unverified
3FedASAMACC@1-100Clients46.58—Unverified
4FedSAMACC@1-100Clients44.84—Unverified
5FedAvgACC@1-100Clients41.27—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.27—Unverified
2FedASAMACC@1-100Clients47.78—Unverified
3FedSAM + SWAACC@1-100Clients46.47—Unverified
4FedSAMACC@1-100Clients46.05—Unverified
5FedAvgACC@1-100Clients42.17—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients49.17—Unverified
2FedSAM + SWAACC@1-100Clients47.96—Unverified
3FedASAMACC@1-100Clients45.61—Unverified
4FedSAMACC@1-100Clients44.73—Unverified
5FedAvgACC@1-100Clients40.43—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.01—Unverified
2FedSAM + SWAACC@1-100Clients39.3—Unverified
3FedASAMACC@1-100Clients36.04—Unverified
4FedSAMACC@1-100Clients31.04—Unverified
5FedAvgACC@1-100Clients30.25—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.97—Unverified
2FedASAM + SWAACC@1-100Clients54.79—Unverified
3FedSAM + SWAACC@1-100Clients53.67—Unverified
4FedSAMACC@1-100Clients53.39—Unverified
5FedAvgACC@1-100Clients50.25—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.5—Unverified
2FedSAM + SWAACC@1-100Clients54.36—Unverified
3FedASAM + SWAACC@1-100Clients54.1—Unverified
4FedSAMACC@1-100Clients53.97—Unverified
5FedAvgACC@1-100Clients50.66—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.81—Unverified
2FedSAMACC@1-100Clients54.01—Unverified
3FedSAM + SWAACC@1-100Clients53.9—Unverified
4FedASAM + SWAACC@1-100Clients53.86—Unverified
5FedAvgACC@1-100Clients49.92—Unverified
#ModelMetricClaimedVerifiedStatus
1AdaBestAverage Top-1 Accuracy56.2—Unverified