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 901–950 of 6771 papers

TitleStatusHype
A New Implementation of Federated Learning for Privacy and Security Enhancement—0
Algorithms for Collaborative Machine Learning under Statistical Heterogeneity—0
A new framework for prognostics in decentralized industries: Enhancing fairness, security, and transparency through Blockchain and Federated Learning—0
Byzantine-Resilient High-Dimensional Federated Learning—0
A Closer Look at Personalization in Federated Image Classification—0
Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning using Independent Component Analysis—0
Coded Computing for Federated Learning at the Edge—0
Coded Matrix Computations for D2D-enabled Linearized Federated Learning—0
A New Dimensionality Reduction Method Based on Hensel's Compression for Privacy Protection in Federated Learning—0
An Evaluation of Non-Contrastive Self-Supervised Learning for Federated Medical Image Analysis—0
Collaborative Distributed Machine Learning—0
On the Convergence of Federated Averaging under Partial Participation for Over-parameterized Neural Networks—0
Buffer is All You Need: Defending Federated Learning against Backdoor Attacks under Non-iids via Buffering—0
Buffered Asynchronous Secure Aggregation for Cross-Device Federated Learning—0
Abnormal Local Clustering in Federated Learning—0
Fed-KAN: Federated Learning with Kolmogorov-Arnold Networks for Traffic Prediction—0
Federated Learning in Mobile Edge Networks: A Comprehensive Survey—0
An Enhanced Federated Prototype Learning Method under Domain Shift—0
Budgeted Online Selection of Candidate IoT Clients to Participate in Federated Learning—0
Clustering Algorithm to Detect Adversaries in Federated Learning—0
Building Gradient Bridges: Label Leakage from Restricted Gradient Sharing in Federated Learning—0
Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates—0
Byzantine-resilient Federated Learning Employing Normalized Gradients on Non-IID Datasets—0
Byzantine-Resilient Federated Learning at Edge—0
Byzantine-Resilient Federated Learning via Distributed Optimization—0
Byzantine-resilient Federated Learning With Adaptivity to Data Heterogeneity—0
Budgeted Online Model Selection and Fine-Tuning via Federated Learning—0
Byzantine-Resilient Over-the-Air Federated Learning under Zero-Trust Architecture—0
Byzantine-Resilient Secure Aggregation for Federated Learning Without Privacy Compromises—0
Byzantine-Resilient Secure Federated Learning—0
Byzantine-Resilient Zero-Order Optimization for Communication-Efficient Heterogeneous Federated Learning—0
ByzSecAgg: A Byzantine-Resistant Secure Aggregation Scheme for Federated Learning Based on Coded Computing and Vector Commitment—0
A Differentially Private Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations—0
Byzantine-Robust and Privacy-Preserving Framework for FedML—0
An Energy Optimized Specializing DAG Federated Learning based on Event Triggered Communication—0
Decision Models for Selecting Federated Learning Architecture Patterns—0
Byzantine-Robust Decentralized Federated Learning—0
Clustering-Based Evolutionary Federated Multiobjective Optimization and Learning—0
Byzantine-Robust Federated Learning: An Overview With Focus on Developing Sybil-based Attacks to Backdoor Augmented Secure Aggregation Protocols—0
Byzantine-Robust Federated Learning over Ring-All-Reduce Distributed Computing—0
Byzantine-robust Federated Learning through Spatial-temporal Analysis of Local Model Updates—0
Byzantine-Robust Federated Learning via Credibility Assessment on Non-IID Data—0
Distributed Non-Convex Optimization with One-Bit Compressors on Heterogeneous Data: Efficient and Resilient Algorithms—0
Byzantine-Robust Federated Learning with Variance Reduction and Differential Privacy—0
Byzantine-Robust Federated Linear Bandits—0
Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging—0
An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning—0
An Experimental Study of Class Imbalance in Federated Learning—0
Byzantines can also Learn from History: Fall of Centered Clipping in Federated Learning—0
An Energy and Carbon Footprint Analysis of Distributed and Federated Learning—0
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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