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 27512800 of 6771 papers

TitleStatusHype
Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency0
The Paradox of Noise: An Empirical Study of Noise-Infusion Mechanisms to Improve Generalization, Stability, and Privacy in Federated Learning0
Data Valuation and Detections in Federated LearningCode1
Robust and Communication-Efficient Federated Domain Adaptation via Random FeaturesCode0
Edge-assisted U-Shaped Split Federated Learning with Privacy-preserving for Internet of Things0
Accurate Autism Spectrum Disorder prediction using Support Vector Classifier based on Federated Learning (SVCFL)0
Decentralized Personalized Online Federated Learning0
Asynchronous Message-Passing and Zeroth-Order Optimization Based Distributed Learning with a Use-Case in Resource Allocation in Communication Networks0
Cross-Silo Federated Learning Across Divergent Domains with Iterative Parameter AlignmentCode0
Input Reconstruction Attack against Vertical Federated Large Language Models0
SaFL: Sybil-aware Federated Learning with Application to Face Recognition0
Blind Federated Learning via Over-the-Air q-QAM0
Device Sampling and Resource Optimization for Federated Learning in Cooperative Edge Networks0
Convergence Analysis of Sequential Federated Learning on Heterogeneous DataCode0
CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centers0
Asynchronous Local Computations in Distributed Bayesian Learning0
Goal-Oriented Wireless Communication Resource Allocation for Cyber-Physical Systems0
Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches0
Communication Efficient and Privacy-Preserving Federated Learning Based on Evolution StrategiesCode0
Sample Complexity of Linear Regression Models for Opinion Formation in NetworksCode0
Hybrid quantum image classification and federated learning for hepatic steatosis diagnosis0
Client Orchestration and Cost-Efficient Joint Optimization for NOMA-Enabled Hierarchical Federated Learning0
Epidemic Decision-making System Based Federated Reinforcement Learning0
Heterogeneous federated collaborative filtering using FAIR: Federated Averaging in Random SubspacesCode0
Dynamic Fair Federated Learning Based on Reinforcement Learning0
Decentralized Federated Learning on the Edge over Wireless Mesh Networks0
FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networks0
Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization0
Federated Learning on Edge Sensing Devices: A Review0
StableFDG: Style and Attention Based Learning for Federated Domain Generalization0
MetisFL: An Embarrassingly Parallelized Controller for Scalable & Efficient Federated Learning Workflows0
A Comprehensive Study on Model Initialization Techniques Ensuring Efficient Federated Learning0
Privacy-preserving design of graph neural networks with applications to vertical federated learning0
FlexTrain: A Dynamic Training Framework for Heterogeneous Devices Environments0
Compression with Exact Error Distribution for Federated Learning0
FedRec+: Enhancing Privacy and Addressing Heterogeneity in Federated Recommendation Systems0
Enhancing Scalability and Reliability in Semi-Decentralized Federated Learning With Blockchain: Trust Penalization and Asynchronous Functionality0
PriPrune: Quantifying and Preserving Privacy in Pruned Federated Learning0
Privacy-preserving Federated Primal-dual Learning for Non-convex and Non-smooth Problems with Model Sparsification0
A Federated Learning Framework for Stenosis Detection0
Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detection0
Maximum Knowledge Orthogonality Reconstruction with Gradients in Federated LearningCode0
Exploring Federated Unlearning: Review, Comparison, and InsightsCode1
Escaping Saddle Points in Heterogeneous Federated Learning via Distributed SGD with Communication Compression0
Efficient Cluster Selection for Personalized Federated Learning: A Multi-Armed Bandit Approach0
RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Adversarial Data ManipulationCode0
Peer-to-Peer Deep Learning for Beyond-5G IoT0
Correlation Aware Sparsified Mean Estimation Using Random Projection0
Adaptive Test-Time Personalization for Federated LearningCode1
Rethinking Semi-Supervised Federated Learning: How to co-train fully-labeled and fully-unlabeled client imaging data0
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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