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 19512000 of 6771 papers

TitleStatusHype
Federated Hierarchical Tensor Networks: a Collaborative Learning Quantum AI-Driven Framework for Healthcare0
Navigating the Future of Federated Recommendation Systems with Foundation Models0
On-Demand Model and Client Deployment in Federated Learning with Deep Reinforcement Learning0
CRSFL: Cluster-based Resource-aware Split Federated Learning for Continuous Authentication0
Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizations0
Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcare0
FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based OptimizationCode1
DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation0
Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning0
MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis0
ACTION: Augmentation and Computation Toolbox for Brain Network Analysis with Functional MRICode1
Multi-level Personalized Federated Learning on Heterogeneous and Long-Tailed Data0
Federated Document Visual Question Answering: A Pilot StudyCode0
Agent-oriented Joint Decision Support for Data Owners in Auction-based Federated Learning0
Privacy-Preserving Edge Federated Learning for Intelligent Mobile-Health SystemsCode0
Age Aware Scheduling for Differentially-Private Federated Learning0
Federated Combinatorial Multi-Agent Multi-Armed Bandits0
Compressed Bayesian Federated Learning for Reliable Passive Radio Sensing in Industrial IoT0
Federated Adaptation for Foundation Model-based RecommendationsCode1
SCALA: Split Federated Learning with Concatenated Activations and Logit Adjustments0
When Foresight Pruning Meets Zeroth-Order Optimization: Efficient Federated Learning for Low-Memory Devices0
Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed BanditCode0
Fast Decentralized Gradient Tracking for Federated Minimax Optimization with Local Updates0
Scalable Vertical Federated Learning via Data Augmentation and Amortized Inference0
FedSC: Provable Federated Self-supervised Learning with Spectral Contrastive Objective over Non-i.i.d. Data0
FedStale: leveraging stale client updates in federated learningCode0
Ranking-based Client Selection with Imitation Learning for Efficient Federated Learning0
Federated Learning for Collaborative Inference Systems: The Case of Early Exit Networks0
IPFed: Identity protected federated learning for user authentication0
Differentially Private Federated Learning without Noise Addition: When is it Possible?0
Interpretable Data Fusion for Distributed Learning: A Representative Approach via Gradient Matching0
Exploring the Efficacy of Federated-Continual Learning Nodes with Attention-Based Classifier for Robust Web Phishing Detection: An Empirical Investigation0
Federated Learning for Drowsiness Detection in Connected Vehicles0
LightTR: A Lightweight Framework for Federated Trajectory RecoveryCode0
GI-SMN: Gradient Inversion Attack against Federated Learning without Prior Knowledge0
Communication-Efficient Federated Learning with Adaptive Compression under Dynamic Bandwidth0
The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscape0
Snake Learning: A Communication- and Computation-Efficient Distributed Learning Framework for 6G0
LIFL: A Lightweight, Event-driven Serverless Platform for Federated Learning0
Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation0
FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfer0
Towards Fairness in Provably Communication-Efficient Federated Recommender Systems0
Holistic Evaluation Metrics: Use Case Sensitive Evaluation Metrics for Federated Learning0
A Survey on Contribution Evaluation in Vertical Federated LearningCode0
An Information Theoretic Perspective on Conformal Prediction0
Histogram-Based Federated XGBoost using Minimal Variance Sampling for Federated Tabular Data0
A Federated Learning Benchmark on Tabular Data: Comparing Tree-Based Models and Neural Networks0
Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case0
Privacy-aware Berrut Approximated Coded Computing for Federated Learning0
Navigating Heterogeneity and Privacy in One-Shot Federated Learning with Diffusion Models0
Show:102550
← PrevPage 40 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