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 45014550 of 6771 papers

TitleStatusHype
Inverse Feasibility in Over-the-Air Federated Learning0
FedSysID: A Federated Approach to Sample-Efficient System IdentificationCode0
Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression0
Federated Learning Hyper-Parameter Tuning from a System PerspectiveCode0
Collaborative Training of Medical Artificial Intelligence Models with non-uniform LabelsCode0
FedCut: A Spectral Analysis Framework for Reliable Detection of Byzantine Colluders0
Multi-Job Intelligent Scheduling with Cross-Device Federated Learning0
Vertical Federated Learning: Concepts, Advances and Challenges0
Event-Triggered Decentralized Federated Learning over Resource-Constrained Edge Devices0
GitFL: Adaptive Asynchronous Federated Learning using Version Control0
Online Federated Learning via Non-Stationary Detection and Adaptation amidst Concept Drift0
Exploiting Personalized Invariance for Better Out-of-distribution Generalization in Federated Learning0
SPIN: Simulated Poisoning and Inversion Network for Federated Learning-Based 6G Vehicular Networks0
DPD-fVAE: Synthetic Data Generation Using Federated Variational Autoencoders With Differentially-Private DecoderCode0
Mask Off: Analytic-based Malware Detection By Transfer Learning and Model Personalization0
Learning to Generate Image Embeddings with User-level Differential Privacy0
Scalable Collaborative Learning via Representation Sharing0
Non-Coherent Over-the-Air Decentralized Gradient Descent0
Personalized Federated Learning with Hidden Information on Personalized Prior0
Personalized Federated Learning for Multi-task Fault Diagnosis of Rotating Machinery0
Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine0
FedSiam-DA: Dual-aggregated Federated Learning via Siamese Network under Non-IID Data0
Resource Allocation of Federated Learning for the Metaverse with Mobile Augmented Reality0
Quantifying the Impact of Label Noise on Federated Learning0
Bayesian Federated Neural Matching that Completes Full Information0
FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers0
Personalized Federated Learning with Multi-branch Architecture0
Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges0
Optimal Privacy Preserving for Federated Learning in Mobile Edge Computing0
FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Model0
Feature Correlation-guided Knowledge Transfer for Federated Self-supervised Learning0
Federated Learning for Autoencoder-based Condition Monitoring in the Industrial Internet of Things0
Adaptive Federated Minimax Optimization with Lower Complexities0
Watermarking in Secure Federated Learning: A Verification Framework Based on Client-Side Backdooring0
Differentially Private Vertical Federated Learning0
Towards Privacy-Aware Causal Structure Learning in Federated SettingCode0
Quantum Split Neural Network Learning using Cross-Channel Pooling0
A Federated Approach to Predicting Emojis in Hindi TweetsCode0
Federated Unsupervised Visual Representation Learning via Exploiting General Content and Personal Style0
From Competition to Collaboration: Making Toy Datasets on Kaggle Clinically Useful for Chest X-Ray Diagnosis Using Federated Learning0
Warmup and Transfer Knowledge-Based Federated Learning Approach for IoT Continuous Authentication0
Secure Aggregation Is Not All You Need: Mitigating Privacy Attacks with Noise Tolerance in Federated Learning0
Robust Smart Home Face Recognition under Starving Federated DataCode0
Robust Federated Learning against both Data Heterogeneity and Poisoning Attack via Aggregation Optimization0
Framework Construction of an Adversarial Federated Transfer Learning Classifier0
Almost Tight Error Bounds on Differentially Private Continual Counting0
Resource-Aware Heterogeneous Federated Learning using Neural Architecture Search0
Knowledge Distillation for Federated Learning: a Practical Guide0
Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Design0
Federated Learning Using Three-Operator ADMM0
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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