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 951–1000 of 6771 papers

TitleStatusHype
An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning—0
CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balance—0
An Incentive Mechanism for Federated Learning in Wireless Cellular network: An Auction Approach—0
CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning—0
Caesar: A Low-deviation Compression Approach for Efficient Federated Learning—0
CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centers—0
CAFE: Catastrophic Data Leakage in Federated Learning—0
An Energy and Carbon Footprint Analysis of Distributed and Federated Learning—0
Bristle: Decentralized Federated Learning in Byzantine, Non-i.i.d. Environments—0
MUDGUARD: Taming Malicious Majorities in Federated Learning using Privacy-Preserving Byzantine-Robust Clustering—0
A Client-level Assessment of Collaborative Backdoor Poisoning in Non-IID Federated Learning—0
An Information Theoretic Perspective on Conformal Prediction—0
Communication and Energy Efficient Slimmable Federated Learning via Superposition Coding and Successive Decoding—0
Can collaborative learning be private, robust and scalable?—0
Can Decentralized Learning be more robust than Federated Learning?—0
Can Fair Federated Learning reduce the need for Personalisation?—0
Communication and Energy Efficient Wireless Federated Learning with Intrinsic Privacy—0
Bridging the Gap Between Foundation Models and Heterogeneous Federated Learning—0
CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression—0
Canoe : A System for Collaborative Learning for Neural Nets—0
Can Public Large Language Models Help Private Cross-device Federated Learning?—0
An End-to-End Encrypted Neural Network for Gradient Updates Transmission in Federated Learning—0
Can We Theoretically Quantify the Impacts of Local Updates on the Generalization Performance of Federated Learning?—0
Can We Trust the Similarity Measurement in Federated Learning?—0
Can You Really Backdoor Federated Learning?—0
A decentralized aggregation mechanism for training deep learning models using smart contract system for bank loan prediction—0
Capitalization Normalization for Language Modeling with an Accurate and Efficient Hierarchical RNN Model—0
FLBench: A Benchmark Suite for Federated Learning—0
CAPT: Class-Aware Prompt Tuning for Federated Long-Tailed Learning with Vision-Language Model—0
AnoFel: Supporting Anonymity for Privacy-Preserving Federated Learning—0
Caring Without Sharing: A Federated Learning Crowdsensing Framework for Diversifying Representation of Cities—0
Bridging Data Islands: Geographic Heterogeneity-Aware Federated Learning for Collaborative Remote Sensing Semantic Segmentation—0
Bridging Data Barriers among Participants: Assessing the Potential of Geoenergy through Federated Learning—0
CatFedAvg: Optimising Communication-efficiency and Classification Accuracy in Federated Learning—0
CATFL: Certificateless Authentication-based Trustworthy Federated Learning for 6G Semantic Communications—0
CC-FedAvg: Computationally Customized Federated Averaging—0
CCVA-FL: Cross-Client Variations Adaptive Federated Learning for Medical Imaging—0
CD^2-pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learning—0
An Empirical Study of Vulnerability Detection using Federated Learning—0
CDFL: Efficient Federated Human Activity Recognition using Contrastive Learning and Deep Clustering—0
A Non-parametric View of FedAvg and FedProx: Beyond Stationary Points—0
BRFL: A Blockchain-based Byzantine-Robust Federated Learning Model—0
EcoLearn: Optimizing the Carbon Footprint of Federated Learning—0
CELEST: Federated Learning for Globally Coordinated Threat Detection—0
CELLM: An Efficient Communication in Large Language Models Training for Federated Learning—0
Anonymizing Data for Privacy-Preserving Federated Learning—0
Celtibero: Robust Layered Aggregation for Federated Learning—0
Breaking the Memory Wall for Heterogeneous Federated Learning via Model Splitting—0
An Empirical Study of the Impact of Federated Learning on Machine Learning Model Accuracy—0
Breaking the Memory Wall for Heterogeneous Federated Learning via Progressive Training—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