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 62016250 of 6771 papers

TitleStatusHype
Faster Non-Convex Federated Learning via Global and Local Momentum0
Design and Analysis of Uplink and Downlink Communications for Federated Learning0
Dynamic Clustering in Federated Learning0
TornadoAggregate: Accurate and Scalable Federated Learning via the Ring-Based Architecture0
Probabilistic Federated Learning of Neural Networks Incorporated with Global Posterior Information0
SoK: Training Machine Learning Models over Multiple Sources with Privacy Preservation0
FedSiam: Towards Adaptive Federated Semi-Supervised Learning0
Accurate and Fast Federated Learning via Combinatorial Multi-Armed Bandits0
Mitigating Bias in Federated Learning0
Federated Learning with Heterogeneous Labels and Models for Mobile Activity Monitoring0
FAT: Federated Adversarial Training0
Federated Learning for Personalized Humor Recognition0
Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications0
Blockchain Assisted Decentralized Federated Learning (BLADE-FL) with Lazy Clients0
Second-Order Guarantees in Federated Learning0
MYSTIKO : : Cloud-Mediated, Private, Federated Gradient Descent0
A Systematic Literature Review on Federated Learning: From A Model Quality Perspective0
Federated Marginal Personalization for ASR Rescoring0
Federated Learning for Spoken Language Understanding0
Communication-Efficient Federated Distillation0
Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach0
Fast-Convergent Federated Learning with Adaptive Weighting0
Edge-assisted Democratized Learning Towards Federated Analytics0
Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Management0
LocKedge: Low-Complexity Cyberattack Detection in IoT Edge Computing0
Advancements of federated learning towards privacy preservation: from federated learning to split learning0
Optimizing Resource-Efficiency for Federated Edge Intelligence in IoT Networks0
Wyner-Ziv Estimators for Distributed Mean Estimation with Side Information and OptimizationCode0
Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan0
Improving Federated Relational Data Modeling via Basis Alignment and Weight Penalty0
Federated learning with class imbalance reduction0
LINDT: Tackling Negative Federated Learning with Local Adaptation0
A decentralized aggregation mechanism for training deep learning models using smart contract system for bank loan prediction0
FedEval: A Holistic Evaluation Framework for Federated Learning0
FLaaS: Federated Learning as a Service0
Stochastic Client Selection for Federated Learning with Volatile Clients0
Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networks0
Private Wireless Federated Learning with Anonymous Over-the-Air Computation0
Budgeted Online Selection of Candidate IoT Clients to Participate in Federated Learning0
Dynamic backdoor attacks against federated learning0
2CP: Decentralized Protocols to Transparently Evaluate Contributivity in Blockchain Federated Learning Environments0
FedRec: Federated Learning of Universal Receivers over Fading ChannelsCode0
CatFedAvg: Optimising Communication-efficiency and Classification Accuracy in Federated Learning0
Hybrid Federated and Centralized Learning0
Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations0
Federated Multi-Mini-Batch: An Efficient Training Approach to Federated Learning in Non-IID Environments0
Fed-Focal Loss for imbalanced data classification in Federated Learning0
Coded Computing for Low-Latency Federated Learning over Wireless Edge Networks0
Heterogeneous Data-Aware Federated Learning0
A Novel Privacy-Preserved Recommender System Framework based on 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