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

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Showing 4501–4550 of 6771 papers

TitleStatusHype
Find Your Friends: Personalized Federated Learning with the Right Collaborators—0
Find Your Optimal Assignments On-the-fly: A Holistic Framework for Clustered Federated Learning—0
Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT—0
Fine-Tuning Foundation Models with Federated Learning for Privacy Preserving Medical Time Series Forecasting—0
Federated Asymptotics: a model to compare federated learning algorithms—0
Fine-tuning Multimodal Transformers on Edge: A Parallel Split Learning Approach—0
Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients—0
Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data—0
Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning—0
FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk—0
First Analysis of Local GD on Heterogeneous Data—0
Fisher Information-based Efficient Curriculum Federated Learning with Large Language Models—0
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification—0
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout—0
FLaaS: Federated Learning as a Service—0
FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Search—0
FLAG: Fast Label-Adaptive Aggregation for Multi-label Classification in Federated Learning—0
FLAGS Framework for Comparative Analysis of Federated Learning Algorithms—0
FLAME: Adaptive and Reactive Concept Drift Mitigation for Federated Learning Deployments—0
FLAME: A Federated Learning Approach for Multi-Modal RF Fingerprinting—0
FLAME: Federated Learning Across Multi-device Environments—0
FLAMe: Federated Learning with Attention Mechanism using Spatio-Temporal Keypoint Transformers for Pedestrian Fall Detection in Smart Cities—0
FL-APU: A Software Architecture to Ease Practical Implementation of Cross-Silo Federated Learning—0
FLARE: A New Federated Learning Framework with Adjustable Learning Rates over Resource-Constrained Wireless Networks—0
FLARE: Detection and Mitigation of Concept Drift for Federated Learning based IoT Deployments—0
Flashback: Understanding and Mitigating Forgetting in Federated Learning—0
FLASH: Federated Learning Across Simultaneous Heterogeneities—0
FLASH: Federated Learning-Based LLMs for Advanced Query Processing in Social Networks through RAG—0
FLBoost: On-the-Fly Fine-tuning Boosts Federated Learning via Data-free Distillation—0
FLCC: Efficient Distributed Federated Learning on IoMT over CSMA/CA—0
FLCert: Provably Secure Federated Learning against Poisoning Attacks—0
FLECS-CGD: A Federated Learning Second-Order Framework via Compression and Sketching with Compressed Gradient Differences—0
FLEdge: Benchmarking Federated Machine Learning Applications in Edge Computing Systems—0
FLEDGE: Ledger-based Federated Learning Resilient to Inference and Backdoor Attacks—0
FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction—0
Improving Local Training in Federated Learning via Temperature Scaling—0
FlexFed: Mitigating Catastrophic Forgetting in Heterogeneous Federated Learning in Pervasive Computing Environments—0
Flexible Differentially Private Vertical Federated Learning with Adaptive Feature Embeddings—0
Flexible Parallel Learning in Edge Scenarios: Communication, Computational and Energy Cost—0
FlexTrain: A Dynamic Training Framework for Heterogeneous Devices Environments—0
FL Games: A federated learning framework for distribution shifts—0
FL Games: A Federated Learning Framework for Distribution Shifts—0
FLGo: A Fully Customizable Federated Learning Platform—0
FL-GUARD: A Holistic Framework for Run-Time Detection and Recovery of Negative Federated Learning—0
FLHetBench: Benchmarking Device and State Heterogeneity in Federated Learning—0
FLHub: a Federated Learning model sharing service—0
FLIGAN: Enhancing Federated Learning with Incomplete Data using GAN—0
FLINT: A Platform for Federated Learning Integration—0
Privacy-Preserving Federated Learning via Dataset Distillation—0
FLIPS: Federated Learning using Intelligent Participant Selection—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