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 22512300 of 6771 papers

TitleStatusHype
A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things0
A Federated Online Restless Bandit Framework for Cooperative Resource Allocation0
Adap DP-FL: Differentially Private Federated Learning with Adaptive Noise0
Concept Drift Detection in Federated Networked Systems0
Concept drift detection and adaptation for federated and continual learning0
Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning0
Compute-Update Federated Learning: A Lattice Coding Approach Over-the-Air0
A Survey on Efficient Federated Learning Methods for Foundation Model Training0
A Federated Multi-View Deep Learning Framework for Privacy-Preserving Recommendations0
Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approach0
Computation- and Communication-Efficient Online FL for Resource-Constrained Aerial Vehicles0
A Survey on Decentralized Federated Learning0
Computation and Communication Efficient Lightweighting Vertical Federated Learning for Smart Building IoT0
Adaptive Model Pruning and Personalization for Federated Learning over Wireless Networks0
A Federated Learning Scheme for Neuro-developmental Disorders: Multi-Aspect ASD Detection0
AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity0
Accelerating Federated Learning via Momentum Gradient Descent0
Federated Learning on Stochastic Neural Networks0
Compression with Exact Error Distribution for Federated Learning0
Compression Boosts Differentially Private Federated Learning0
A Survey on Cluster-based Federated Learning0
Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data0
Compressed Bayesian Federated Learning for Reliable Passive Radio Sensing in Industrial IoT0
A Survey on Class Imbalance in Federated Learning0
A Federated Learning Platform as a Service for Advancing Stroke Management in European Clinical Centers0
Compressed and distributed least-squares regression: convergence rates with applications to Federated Learning0
A Survey on Blockchain-Based Federated Learning and Data Privacy0
Compositional federated learning: Applications in distributionally robust averaging and meta learning0
Composite federated learning with heterogeneous data0
A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency0
A Federated learning model for Electric Energy management using Blockchain Technology0
AdaFedFR: Federated Face Recognition with Adaptive Inter-Class Representation Learning0
Complex-valued Federated Learning with Differential Privacy and MRI Applications0
Completion Time Minimization of Fog-RAN-Assisted Federated Learning With Rate-Splitting Transmission0
A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy0
Completely Heterogeneous Federated Learning0
Complement Sparsification: Low-Overhead Model Pruning for Federated Learning0
A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective0
A Federated Learning Framework for Smart Grids: Securing Power Traces in Collaborative Learning0
Competitive Advantage Attacks to Decentralized Federated Learning0
Comparison of Privacy-Preserving Distributed Deep Learning Methods in Healthcare0
A Survey of Model Extraction Attacks and Defenses in Distributed Computing Environments0
Comparing privacy notions for protection against reconstruction attacks in machine learning0
Comparing Federated Stochastic Gradient Descent and Federated Averaging for Predicting Hospital Length of Stay0
A Survey of Mobile Computing for the Visually Impaired0
A Federated Learning Framework for Stenosis Detection0
AdaFed: Fair Federated Learning via Adaptive Common Descent Direction0
Accelerating Federated Learning over Reliability-Agnostic Clients in Mobile Edge Computing Systems0
FedED: Federated Learning via Ensemble Distillation for Medical Relation Extraction0
FedEmbed: Personalized Private Federated Learning0
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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