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 5351–5400 of 6771 papers

TitleStatusHype
On the Convergence of Zeroth-Order Federated Tuning for Large Language Models—0
On the Convergence Time of Federated Learning Over Wireless Networks Under Imperfect CSI—0
On the Decentralization of Blockchain-enabled Asynchronous Federated Learning—0
Communication Efficient Federated Learning with Energy Awareness over Wireless Networks—0
On the Design of Communication-Efficient Federated Learning for Health Monitoring—0
On the dynamics of multi agent nonlinear filtering and learning—0
On the Effect of Defections in Federated Learning and How to Prevent Them—0
On the Energy and Communication Efficiency Tradeoffs in Federated and Multi-Task Learning—0
On the Distributed Evaluation of Generative Models—0
Breaking the Communication-Privacy-Accuracy Tradeoff with f-Differential Privacy—0
On the Federated Learning Framework for Cooperative Perception—0
On-the-fly Resource-Aware Model Aggregation for Federated Learning in Heterogeneous Edge—0
On the Generalization of Wasserstein Robust Federated Learning—0
On the Hardness of Robustness Transfer: A Perspective from Rademacher Complexity over Symmetric Difference Hypothesis Space—0
On the Impact of Data Heterogeneity in Federated Learning Environments with Application to Healthcare Networks—0
On the Impact of Device and Behavioral Heterogeneity in Federated Learning—0
On the Importance of Trust in Next-Generation Networked CPS Systems: An AI Perspective—0
On the Initial Behavior Monitoring Issues in Federated Learning—0
On the Local Cache Update Rules in Streaming Federated Learning—0
On the Performance of Gradient Tracking with Local Updates—0
On the Practicality of Differential Privacy in Federated Learning by Tuning Iteration Times—0
On the Role of Server Momentum in Federated Learning—0
On the Stability Analysis of Open Federated Learning Systems—0
On the Tradeoff between Energy, Precision, and Accuracy in Federated Quantized Neural Networks—0
On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data—0
Ontology- and LLM-based Data Harmonization for Federated Learning in Healthcare—0
Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcare—0
OpenFL: An open-source framework for Federated Learning—0
Open-Source AI-based SE Tools: Opportunities and Challenges of Collaborative Software Learning—0
Open-Source LLM-Driven Federated Transformer for Predictive IoV Management—0
Opportunistic Federated Learning: An Exploration of Egocentric Collaboration for Pervasive Computing Applications—0
Optimal Batch Allocation for Wireless Federated Learning—0
Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy—0
Optimal Complexity in Non-Convex Decentralized Learning over Time-Varying Networks—0
Optimal Federated Learning for Functional Mean Estimation under Heterogeneous Privacy Constraints—0
Optimal Federated Learning for Nonparametric Regression with Heterogeneous Distributed Differential Privacy Constraints—0
Optimal Gradient Compression for Distributed and Federated Learning—0
Optimal Importance Sampling for Federated Learning—0
Optimal Model Averaging: Towards Personalized Collaborative Learning—0
Optimal Privacy Preserving for Federated Learning in Mobile Edge Computing—0
Optimal Rate Adaption in Federated Learning with Compressed Communications—0
Optimal Rates for O(1)-Smooth DP-SCO with a Single Epoch and Large Batches—0
Optimal Strategies for Federated Learning Maintaining Client Privacy—0
Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning—0
Optimisation of federated learning settings under statistical heterogeneity variations—0
Optimising Communication Overhead in Federated Learning Using NSGA-II—0
Optimization-Based GenQSGD for Federated Edge Learning—0
Optimization Design for Federated Learning in Heterogeneous 6G Networks—0
Optimization of User Selection and Bandwidth Allocation for Federated Learning in VLC/RF Systems—0
Optimizing Asynchronous Federated Learning: A~Delicate Trade-Off Between Model-Parameter Staleness and Update Frequency—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