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 37513800 of 6771 papers

TitleStatusHype
Personalized Federated Learning via Dual-Prompt Optimization and Cross Fusion0
Personalized Federated Learning via Gradient Modulation for Heterogeneous Text Summarization0
Personalized Federated Learning via Learning Dynamic Graphs0
Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach0
Personalized Federated Learning with Contextualized Generalization0
Personalized Federated Learning with Clustering: Non-IID Heart Rate Variability Data Application0
Personalized Federated Learning with Communication Compression0
Personalized Federated Learning with Attention-based Client Selection0
Personalized Federated Learning with Adaptive Feature Aggregation and Knowledge Transfer0
Personalized Federated Learning with Exact Stochastic Gradient Descent0
Personalized Federated Learning with Hidden Information on Personalized Prior0
Personalized Federated Learning with Local Attention0
Personalized Federated Learning with Multi-branch Architecture0
Personalized Federated Recommendation via Joint Representation Learning, User Clustering, and Model Adaptation0
Personalized Federated Recommender Systems with Private and Partially Federated AutoEncoders0
Personalized Federated Training of Diffusion Models with Privacy Guarantees0
Personalized Federated X -armed Bandit0
Personalized Graph Federated Learning with Differential Privacy0
Personalized Heterogeneous Federated Learning with Gradient Similarity0
Personalized Hierarchical Split Federated Learning in Wireless Networks0
Personalized Interpretation on Federated Learning: A Virtual Concepts approach0
Personalized Neural Architecture Search for Federated Learning0
Personalized Over-the-Air Federated Learning with Personalized Reconfigurable Intelligent Surfaces0
Personalized Quantum Federated Learning for Privacy Image Classification0
Personalized Semantics Excitation for Federated Image Classification0
Personalized Semi-Supervised Federated Learning for Human Activity Recognition0
Personalized Subgraph Federated Learning with Differentiable Auxiliary Projections0
Personalized Wireless Federated Learning for Large Language Models0
Personalizing Federated Learning for Hierarchical Edge Networks with Non-IID Data0
Personalizing Federated Learning with Over-the-Air Computations0
Personalizing Low-Rank Bayesian Neural Networks Via Federated Learning0
Personalizing or Not: Dynamically Personalized Federated Learning with Incentives0
PFAttack: Stealthy Attack Bypassing Group Fairness in Federated Learning0
PFedDST: Personalized Federated Learning with Decentralized Selection Training0
pFedES: Model Heterogeneous Personalized Federated Learning with Feature Extractor Sharing0
pFedFair: Towards Optimal Group Fairness-Accuracy Trade-off in Heterogeneous Federated Learning0
pFedGame -- Decentralized Federated Learning using Game Theory in Dynamic Topology0
pFedGPA: Diffusion-based Generative Parameter Aggregation for Personalized Federated Learning0
pFedMxF: Personalized Federated Class-Incremental Learning with Mixture of Frequency Aggregation0
pFedSim: Similarity-Aware Model Aggregation Towards Personalized Federated Learning0
pFedSOP : Accelerating Training Of Personalized Federated Learning Using Second-Order Optimization0
pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Data0
pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning0
pFLFE: Cross-silo Personalized Federated Learning via Feature Enhancement on Medical Image Segmentation0
PFL-GAN: When Client Heterogeneity Meets Generative Models in Personalized Federated Learning0
Phoenix: A Federated Generative Diffusion Model0
Photon: Federated LLM Pre-Training0
Picking Daisies in Private: Federated Learning from Small Datasets0
IP-FL: Incentivized and Personalized Federated Learning0
PiPar: Pipeline Parallelism for Collaborative Machine 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