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 15011550 of 6771 papers

TitleStatusHype
Federated Learning Approach to Mitigate Water Wastage0
Federated Diabetes Prediction in Canadian Adults Using Real-world Cross-Province Primary Care Data0
FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts0
RFID based Health Adherence Medicine Case Using Fair Federated LearningCode0
FedGS: Federated Gradient Scaling for Heterogeneous Medical Image SegmentationCode0
The Key of Parameter Skew in Federated Learning0
Federated Clustering: An Unsupervised Cluster-Wise Training for Decentralized Data Distributions0
Security Assessment of Hierarchical Federated Deep LearningCode0
PolypDB: A Curated Multi-Center Dataset for Development of AI Algorithms in ColonoscopyCode1
Parameter-Efficient Transfer Learning under Federated Learning for Automatic Speech Recognition0
Differentially Private Stochastic Gradient Descent with Fixed-Size Minibatches: Tighter RDP Guarantees with or without ReplacementCode0
Federated Learning of Large ASR Models in the Real World0
Federated Frank-Wolfe AlgorithmCode0
Sequential Federated Learning in Hierarchical Architecture on Non-IID Datasets0
Towards Robust Federated Image Classification: An Empirical Study of Weight Selection Strategies in Manufacturing0
Byzantine-resilient Federated Learning Employing Normalized Gradients on Non-IID Datasets0
Mitigating Noise Detriment in Differentially Private Federated Learning with Model Pre-training0
Seamless Integration: Sampling Strategies in Federated Learning Systems0
Addressing Heterogeneity in Federated Learning: Challenges and Solutions for a Shared Production Environment0
Federated Graph Learning with Structure Proxy AlignmentCode0
FEDKIM: Adaptive Federated Knowledge Injection into Medical Foundation ModelsCode0
FedKBP: Federated dose prediction framework for knowledge-based planning in radiation therapy0
DRL-Based Resource Allocation for Motion Blur Resistant Federated Self-Supervised Learning in IoVCode0
Twin Sorting Dynamic Programming Assisted User Association and Wireless Bandwidth Allocation for Hierarchical Federated Learning0
FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge InjectionCode0
Enhancing Object Detection with Hybrid dataset in Manufacturing Environments: Comparing Federated Learning to Conventional Techniques0
A Multivocal Literature Review on Privacy and Fairness in Federated Learning0
Mitigating Backdoor Attacks in Federated Learning via Flipping Weight Updates of Low-Activation Input Neurons0
A Hassle-free Algorithm for Private Learning in Practice: Don't Use Tree Aggregation, Use BLTs0
RBLA: Rank-Based-LoRA-Aggregation for Fine-tuning Heterogeneous Models in FLaaS0
The Power of Bias: Optimizing Client Selection in Federated Learning with Heterogeneous Differential Privacy0
Federated Sequence-to-Sequence Learning for Load Disaggregation from Unbalanced Low-Resolution Smart Meter Data0
Random Gradient Masking as a Defensive Measure to Deep Leakage in Federated Learning0
Addressing Skewed Heterogeneity via Federated Prototype Rectification with Personalization0
Federated Fairness Analytics: Quantifying Fairness in Federated LearningCode0
Communication-robust and Privacy-safe Distributed Estimation for Heterogeneous Community-level Behind-the-meter Solar Power Generation0
FedQUIT: On-Device Federated Unlearning via a Quasi-Competent Virtual Teacher0
Enhancing Equitable Access to AI in Housing and Homelessness System of Care through Federated Learning0
Meta-Learning for Federated Face Recognition in Imbalanced Data Regimes0
An Adaptive Differential Privacy Method Based on Federated Learning0
Prioritizing Modalities: Flexible Importance Scheduling in Federated Multimodal Learning0
Heterogeneity: An Open Challenge for Federated On-board Machine Learning0
Centralized and Federated Heart Disease Classification Models Using UCI Dataset and their Shapley-value Based InterpretabilityCode0
Online-Score-Aided Federated Learning: Taming the Resource Constraints in Wireless Networks0
Decentralized Health Intelligence Network (DHIN)0
Understanding Byzantine Robustness in Federated Learning with A Black-box Server0
Personalized Federated Learning for improving radar based precipitation nowcasting on heterogeneous areas0
On the Convergence of a Federated Expectation-Maximization Algorithm0
Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data0
FedRobo: Federated Learning Driven Autonomous Inter Robots Communication For Optimal Chemical Sprays0
Show:102550
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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