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 29012950 of 6771 papers

TitleStatusHype
ZooPFL: Exploring Black-box Foundation Models for Personalized Federated LearningCode2
Federated Self-Supervised Learning of Monocular Depth Estimators for Autonomous Vehicles0
Profit: Benchmarking Personalization and Robustness Trade-off in Federated Prompt Tuning0
Utilizing Free Clients in Federated Learning for Focused Model Enhancement0
Kick Bad Guys Out! Conditionally Activated Anomaly Detection in Federated Learning with Zero-Knowledge Proof Verification0
FedConv: Enhancing Convolutional Neural Networks for Handling Data Heterogeneity in Federated LearningCode1
The Role of Federated Learning in a Wireless World with Foundation Models0
Towards Understanding Generalization and Stability Gaps between Centralized and Decentralized Federated Learning0
Over-the-Air Federated Learning with Compressed Sensing: Is Sparsification Necessary?0
PrIeD-KIE: Towards Privacy Preserved Document Key Information Extraction0
Solving a Class of Non-Convex Minimax Optimization in Federated LearningCode0
Inclusive Data Representation in Federated Learning: A Novel Approach Integrating Textual and Visual Prompt0
FedHyper: A Universal and Robust Learning Rate Scheduler for Federated Learning with Hypergradient Descent0
Learning to Prompt Your Domain for Vision-Language Models0
Digital Ethics in Federated Learning0
Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly0
On the Power of Adaptive Weighted Aggregation in Heterogeneous Federated Learning and BeyondCode1
Hire When You Need to: Gradual Participant Recruitment for Auction-based Federated Learning0
Semi-Federated Learning: Convergence Analysis and Optimization of A Hybrid Learning Framework0
Heterogeneous Federated Learning Using Knowledge Codistillation0
Federated Conditional Stochastic Optimization0
zkFL: Zero-Knowledge Proof-based Gradient Aggregation for Federated Learning0
Recent Methodological Advances in Federated Learning for Healthcare0
Enhanced Federated Optimization: Adaptive Unbiased Client Sampling with Reduced Variance0
Federated Wasserstein Distance0
FLEDGE: Ledger-based Federated Learning Resilient to Inference and Backdoor Attacks0
Adversarial Client Detection via Non-parametric Subspace Monitoring in the Internet of Federated Things0
Window-based Model Averaging Improves Generalization in Heterogeneous Federated Learning0
Federated K-means Clustering0
FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models0
Towards Understanding Adversarial Transferability in Federated Learning0
Intelligent Client Selection for Federated Learning using Cellular AutomataCode0
Mind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI ModelsCode0
Bridging the Gap Between Foundation Models and Heterogeneous Federated Learning0
Heterogeneity-Guided Client Sampling: Towards Fast and Efficient Non-IID Federated Learning0
FedLPA: One-shot Federated Learning with Layer-Wise Posterior AggregationCode0
Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers and Gradient Clipping0
The Gift of Feedback: Improving ASR Model Quality by Learning from User Corrections through Federated Learning0
FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of ThingsCode1
Benchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentation0
FedZeN: Towards superlinear zeroth-order federated learning via incremental Hessian estimation0
Mode Connectivity and Data Heterogeneity of Federated Learning0
A Survey of Incremental Transfer Learning: Combining Peer-to-Peer Federated Learning and Domain Incremental Learning for Multicenter CollaborationCode0
A Comprehensive View of Personalized Federated Learning on Heterogeneous Clinical DatasetsCode1
Applications of Federated Learning in IoT for Hyper Personalisation0
Anti-Matthew FL: Bridging the Performance Gap in Federated Learning to Counteract the Matthew Effect0
Collaborative Distributed Machine Learning0
Resisting Backdoor Attacks in Federated Learning via Bidirectional Elections and Individual PerspectiveCode1
Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity LearningCode1
Genetic Algorithm-Based Dynamic Backdoor Attack on Federated Learning-Based Network Traffic ClassificationCode0
Show:102550
← PrevPage 59 of 136Next →

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