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 11011150 of 6771 papers

TitleStatusHype
TempCharBERT: Keystroke Dynamics for Continuous Access Control Based on Pre-trained Language Models0
Federated Learning Client Pruning for Noisy LabelsCode0
Revisiting Ensembling in One-Shot Federated LearningCode0
WassFFed: Wasserstein Fair Federated Learning0
Sketched Adaptive Federated Deep Learning: A Sharp Convergence Analysis0
Model Partition and Resource Allocation for Split Learning in Vehicular Edge Networks0
Using Diffusion Models as Generative Replay in Continual Federated Learning -- What will Happen?0
Client Contribution Normalization for Enhanced Federated Learning0
Federated Split Learning for Human Activity Recognition with Differential Privacy0
Personalized Hierarchical Split Federated Learning in Wireless Networks0
TinyML NLP Scheme for Semantic Wireless Sentiment Classification with Privacy PreservationCode0
Network EM Algorithm for Gaussian Mixture Model in Decentralized Federated Learning0
IPMN Risk Assessment under Federated Learning Paradigm0
QuanCrypt-FL: Quantized Homomorphic Encryption with Pruning for Secure Federated Learning0
DWFL: Enhancing Federated Learning through Dynamic Weighted Averaging0
EPIC: Enhancing Privacy through Iterative Collaboration0
Interplay between Federated Learning and Explainable Artificial Intelligence: a Scoping Review0
FedDP: Privacy-preserving method based on federated learning for histopathology image segmentation0
Fed-LDR: Federated Local Data-infused Graph Creation with Node-centric Model Refinement0
Personalized Federated Learning for Cross-view Geo-localization0
Federated Data-Driven Kalman Filtering for State Estimation0
Cooperation and Personalization on a Seesaw: Choice-based FL for Safe Cooperation in Wireless Networks0
Towards Personalized Federated Learning via Comprehensive Knowledge Distillation0
Optimal Defenses Against Gradient Reconstruction AttacksCode0
Overcoming label shift in targeted federated learning0
Domain Generalization for Cross-Receiver Radio Frequency Fingerprint Identification0
Act in Collusion: A Persistent Distributed Multi-Target Backdoor in Federated Learning0
Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysis0
NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA0
FedSECA: Sign Election and Coordinate-wise Aggregation of Gradients for Byzantine Tolerant Federated LearningCode0
Fed-EC: Bandwidth-Efficient Clustering-Based Federated Learning For Autonomous Visual Robot Navigation0
Navigating Distribution Shifts in Medical Image Analysis: A Survey0
FEDLAD: Federated Evaluation of Deep Leakage Attacks and Defenses0
Formal Logic-guided Robust Federated Learning against Poisoning Attacks0
FedBlock: A Blockchain Approach to Federated Learning against Backdoor Attacks0
ATM: Improving Model Merging by Alternating Tuning and Merging0
Query-Efficient Adversarial Attack Against Vertical Federated Graph LearningCode0
Photon: Federated LLM Pre-Training0
FedPID: An Aggregation Method for Federated Learning0
FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregation0
Automatic Structured Pruning for Efficient Architecture in Federated LearningCode0
FPPL: An Efficient and Non-IID Robust Federated Continual Learning FrameworkCode0
FedReMa: Improving Personalized Federated Learning via Leveraging the Most Relevant ClientsCode0
Masked Autoencoders are Parameter-Efficient Federated Continual LearnersCode0
Federated GNNs for EEG-Based Stroke Assessment0
Anomalous Client Detection in Federated Learning0
Federated Learning Clients Clustering with Adaptation to Data Drifts0
Analysis of regularized federated learning0
Efficient and Robust Regularized Federated RecommendationCode0
Trustworthy Federated Learning: Privacy, Security, and Beyond0
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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