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 61016150 of 6771 papers

TitleStatusHype
FedProf: Selective Federated Learning with Representation Profiling0
Decentralized Federated Learning Preserves Model and Data Privacy0
Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning0
Scaling Federated Learning for Fine-tuning of Large Language Models0
A_Blockchain-Based_Decentralized_Federated_Learning_Framework_with_Committee_Consensus0
On Data Efficiency of Meta-learning0
Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysis0
Self-supervised Cross-silo Federated Neural Architecture Search0
Differential Privacy Meets Federated Learning under Communication Constraints0
Covert Model Poisoning Against Federated Learning: Algorithm Design and Optimization0
FedH2L: Federated Learning with Model and Statistical Heterogeneity0
Artificial Intelligence Driven UAV-NOMA-MEC in Next Generation Wireless Networks0
Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning0
Accuracy and Privacy Evaluations of Collaborative Data Analysis0
Transparent Contribution Evaluation for Secure Federated Learning on Blockchain0
Adaptive Scheduling for Machine Learning Tasks over Networks0
Failure Prediction in Production Line Based on Federated Learning: An Empirical Study0
Untargeted Poisoning Attack Detection in Federated Learning via Behavior Attestation0
Vertical federated learning based on DFP and BFGS0
Time-Correlated Sparsification for Communication-Efficient Federated Learning0
Sum-Rate-Distortion Function for Indirect Multiterminal Source Coding in Federated Learning0
FedNS: Improving Federated Learning for collaborative image classification on mobile clients0
Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge0
Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation0
Federated Learning Based Proactive Handover in Millimeter-wave Vehicular Networks0
Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversary0
Detection of Insider Attacks in Distributed Projected Subgradient Algorithms0
Privacy-Preserving Learning of Human Activity Predictors in Smart Environments0
Probabilistic Inference for Learning from Untrusted Sources0
Auto-weighted Robust Federated Learning with Corrupted Data SourcesCode0
Federated Learning: Opportunities and Challenges0
Towards Energy Efficient Federated Learning over 5G+ Mobile Devices0
Personalized Federated Deep Learning for Pain Estimation From Face ImagesCode0
FedAR: Activity and Resource-Aware Federated Learning Model for Distributed Mobile Robots0
On the Practicality of Differential Privacy in Federated Learning by Tuning Iteration Times0
Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks0
DiPSeN: Differentially Private Self-normalizing Neural Networks For Adversarial Robustness in Federated Learning0
Differentially Private Federated Learning for Cancer PredictionCode0
Federated Intelligence for Active Queue Management in Inter-Domain CongestionCode0
Architectural Patterns for the Design of Federated Learning Systems0
Federated Learning over Noisy Channels: Convergence Analysis and Design Examples0
IPLS : A Framework for Decentralized Federated LearningCode0
Fusion of Federated Learning and Industrial Internet of Things: A Survey0
Federated Learning-Based Risk-Aware Decision toMitigate Fake Task Impacts on CrowdsensingPlatforms0
Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation0
Sself: Robust Federated Learning against Stragglers and Adversaries0
Federated learning using mixture of experts0
End-to-End on-device Federated Learning: A case study0
Fidel: Reconstructing Private Training Samples from Weight Updates in Federated LearningCode0
Few-Round Learning for Federated 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