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 37013750 of 6771 papers

TitleStatusHype
PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models0
PEFT-as-an-Attack! Jailbreaking Language Models during Federated Parameter-Efficient Fine-Tuning0
Pelta: Shielding Transformers to Mitigate Evasion Attacks in Federated Learning0
PEPPER: Empowering User-Centric Recommender Systems over Gossip Learning0
Perfect Privacy for Discriminator-Based Byzantine-Resilient Federated Learning0
PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networks0
PerFedRec++: Enhancing Personalized Federated Recommendation with Self-Supervised Pre-Training0
User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization0
Performance Analysis for Resource Constrained Decentralized Federated Learning Over Wireless Networks0
Performance Analysis of Decentralized Federated Learning Deployments0
Federated Learning with Differential Privacy: Algorithms and Performance Analysis0
Performance Evaluation, Optimization and Dynamic Decision in Blockchain Systems: A Recent Overview0
Performance Guaranteed Poisoning Attacks in Federated Learning: A Sliding Mode Approach0
Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks0
Performance Weighting for Robust Federated Learning Against Corrupted Sources0
Performative Federated Learning: A Solution to Model-Dependent and Heterogeneous Distribution Shifts0
PersA-FL: Personalized Asynchronous Federated Learning0
Personalised Federated Learning: A Combinational Approach0
Personalised Federated Learning On Heterogeneous Feature Spaces0
Personalization Disentanglement for Federated Learning: An explainable perspective0
Personalized Bayesian Federated Learning with Wasserstein Barycenter Aggregation0
Personalized Decentralized Federated Learning with Knowledge Distillation0
Personalized Decentralized Multi-Task Learning Over Dynamic Communication Graphs0
Personalized Federated Deep Reinforcement Learning-based Trajectory Optimization for Multi-UAV Assisted Edge Computing0
Personalized Federated Domain Adaptation for Item-to-Item Recommendation0
Personalized Federated Hypernetworks for Privacy Preservation in Multi-Task Reinforcement Learning0
Personalized Federated Learning: A Meta-Learning Approach0
Personalized Cross-Silo Federated Learning on Non-IID Data0
Personalized federated learning based on feature fusion0
Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework0
Personalized Federated Learning for Heterogeneous Clients with Clustered Knowledge Transfer0
Personalized Federated Learning for Statistical Heterogeneity0
Personalized Federated Learning for Generative AI-Assisted Semantic Communications0
Personalized Federated Learning for Egocentric Video Gaze Estimation with Comprehensive Parameter Frezzing0
Personalized Federated Learning for Cellular VR: Online Learning and Dynamic Caching0
Personalized Federated Learning for Cross-view Geo-localization0
Personalized Federated Learning for improving radar based precipitation nowcasting on heterogeneous areas0
Personalized Federated Learning for Multi-task Fault Diagnosis of Rotating Machinery0
Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach0
Personalized Federated Learning of Probabilistic Models: A PAC-Bayesian Approach0
Personalized Federated Learning of Driver Prediction Models for Autonomous Driving0
Personalized Federated Learning over non-IID Data for Indoor Localization0
Personalized Federated Learning Techniques: Empirical Analysis0
Personalized Federated Learning under Model Dissimilarity Constraints0
Personalized Federated Learning via Sequential Layer Expansion in Representation Learning0
Personalized Federated Learning via Active Sampling0
Personalized Federated Learning via ADMM with Moreau Envelope0
Personalized Federated Learning via Amortized Bayesian Meta-Learning0
Personalized Federated Learning via Backbone Self-Distillation0
Personalized Federated Learning via Convex Clustering0
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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