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 18511900 of 6771 papers

TitleStatusHype
Framework for Co-distillation Driven Federated Learning to Address Class Imbalance in HealthcareCode0
Artificial Intelligence in Pediatric Echocardiography: Exploring Challenges, Opportunities, and Clinical Applications with Explainable AI and Federated Learning0
Evidential Federated Learning for Skin Lesion Image Classification0
Towards efficient compression and communication for prototype-based decentralized learning0
FedRewind: Rewinding Continual Model Exchange for Decentralized Federated Learning0
Time-constrained Federated Learning (FL) in Push-Pull IoT Wireless Access0
FedSub: Introducing class-aware Subnetworks Fusion to Enhance Personalized Federated Learning in Ubiquitous Systems0
SAFELOC: Overcoming Data Poisoning Attacks in Heterogeneous Federated Machine Learning for Indoor Localization0
Efficient Federated Finetuning of Tiny Transformers with Resource-Constrained Devices0
Federated Learning for Discrete Optimal Transport with Large Population under Incomplete Information0
Dual-Criterion Model Aggregation in Federated Learning: Balancing Data Quantity and Quality0
Federated Low-Rank Adaptation with Differential Privacy over Wireless Networks0
On the Convergence of Continual Federated Learning Using Incrementally Aggregated Gradients0
A Stochastic Optimization Framework for Private and Fair Learning From Decentralized DataCode0
Collaborative and Federated Black-box Optimization: A Bayesian Optimization Perspective0
Federated Learning Client Pruning for Noisy LabelsCode0
TempCharBERT: Keystroke Dynamics for Continuous Access Control Based on Pre-trained Language Models0
Revisiting Ensembling in One-Shot Federated LearningCode0
Sketched Adaptive Federated Deep Learning: A Sharp Convergence Analysis0
Model Partition and Resource Allocation for Split Learning in Vehicular Edge Networks0
Movable Antenna-Aided Federated Learning with Over-the-Air Aggregation: Joint Optimization of Positioning, Beamforming, and User Selection0
WassFFed: Wasserstein Fair Federated Learning0
Using Diffusion Models as Generative Replay in Continual Federated Learning -- What will Happen?0
Client Contribution Normalization for Enhanced Federated Learning0
Personalized Hierarchical Split Federated Learning in Wireless Networks0
Federated Split Learning for Human Activity Recognition with Differential Privacy0
TinyML NLP Scheme for Semantic Wireless Sentiment Classification with Privacy PreservationCode0
QuanCrypt-FL: Quantized Homomorphic Encryption with Pruning for Secure Federated Learning0
Network EM Algorithm for Gaussian Mixture Model in Decentralized Federated Learning0
IPMN Risk Assessment under Federated Learning Paradigm0
EPIC: Enhancing Privacy through Iterative Collaboration0
Fed-LDR: Federated Local Data-infused Graph Creation with Node-centric Model Refinement0
DWFL: Enhancing Federated Learning through Dynamic Weighted Averaging0
Personalized Federated Learning for Cross-view Geo-localization0
FedDP: Privacy-preserving method based on federated learning for histopathology image segmentation0
Interplay between Federated Learning and Explainable Artificial Intelligence: a Scoping Review0
FedSECA: Sign Election and Coordinate-wise Aggregation of Gradients for Byzantine Tolerant Federated LearningCode0
Overcoming label shift in targeted federated learning0
NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA0
Domain Generalization for Cross-Receiver Radio Frequency Fingerprint Identification0
Optimal Defenses Against Gradient Reconstruction AttacksCode0
Fed-EC: Bandwidth-Efficient Clustering-Based Federated Learning For Autonomous Visual Robot Navigation0
Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysis0
Cooperation and Personalization on a Seesaw: Choice-based FL for Safe Cooperation in Wireless Networks0
Act in Collusion: A Persistent Distributed Multi-Target Backdoor in Federated Learning0
Federated Data-Driven Kalman Filtering for State Estimation0
Towards Personalized Federated Learning via Comprehensive Knowledge Distillation0
Photon: Federated LLM Pre-Training0
Query-Efficient Adversarial Attack Against Vertical Federated Graph LearningCode0
Formal Logic-guided Robust Federated Learning against Poisoning Attacks0
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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