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 60516100 of 6771 papers

TitleStatusHype
Learning from History for Byzantine Robust OptimizationCode1
Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated LearningCode1
More Industry-friendly: Federated Learning with High Efficient Design0
FedADC: Accelerated Federated Learning with Drift Control0
FedeRank: User Controlled Feedback with Federated Recommender Systems0
Cost-Effective Federated Learning Design0
Towards open and expandable cognitive AI architectures for large-scale multi-agent human-robot collaborative learning0
Personalized Federated Learning with First Order Model OptimizationCode1
CosSGD: Communication-Efficient Federated Learning with a Simple Cosine-Based Quantization0
Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning0
Federated Learning under Importance Sampling0
Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters0
FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring0
Privacy-preserving Decentralized Aggregation for Federated Learning0
Communication-Efficient Federated Learning with Compensated Overlap-FedAvgCode1
Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directions0
Adaptive Histogram-Based Gradient Boosted Trees for Federated Learning0
Privacy-preserving medical image analysis0
Communication-Computation Efficient Secure Aggregation for Federated Learning0
Analysis and Optimal Edge Assignment For Hierarchical Federated Learning on Non-IID Data0
Accurate and Fast Federated Learning via IID and Communication-Aware Grouping0
Privacy Amplification by DecentralizationCode0
Federated Learning in Unreliable and Resource-Constrained Cellular Wireless NetworksCode0
Provable Defense against Privacy Leakage in Federated Learning from Representation PerspectiveCode1
Ditto: Fair and Robust Federated Learning Through PersonalizationCode1
Poisoning Semi-supervised Federated Learning via Unlabeled Data: Attacks and Defenses0
GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs0
Privacy and Robustness in Federated Learning: Attacks and Defenses0
Faster Non-Convex Federated Learning via Global and Local Momentum0
Dynamic Clustering in Federated Learning0
Design and Analysis of Uplink and Downlink Communications for Federated Learning0
TornadoAggregate: Accurate and Scalable Federated Learning via the Ring-Based Architecture0
SoK: Training Machine Learning Models over Multiple Sources with Privacy Preservation0
Accurate and Fast Federated Learning via Combinatorial Multi-Armed Bandits0
FedSiam: Towards Adaptive Federated Semi-Supervised Learning0
Probabilistic Federated Learning of Neural Networks Incorporated with Global Posterior Information0
Unleashing the Tiger: Inference Attacks on Split LearningCode1
Federated Learning with Heterogeneous Labels and Models for Mobile Activity Monitoring0
Mitigating Bias in Federated Learning0
FAT: Federated Adversarial Training0
Federated Learning for Personalized Humor Recognition0
Robust Federated Learning with Noisy LabelsCode1
Blockchain Assisted Decentralized Federated Learning (BLADE-FL) with Lazy Clients0
Second-Order Guarantees in Federated Learning0
Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications0
Federated Learning for Spoken Language Understanding0
A Systematic Literature Review on Federated Learning: From A Model Quality Perspective0
Federated Marginal Personalization for ASR Rescoring0
Communication-Efficient Federated Distillation0
MYSTIKO : : Cloud-Mediated, Private, Federated Gradient Descent0
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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