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 31513200 of 6771 papers

TitleStatusHype
FedGreen: Carbon-aware Federated Learning with Model Size Adaptation0
Federated Transfer-Ordered-Personalized Learning for Driver Monitoring Application0
FedGSM: Efficient Federated Learning for LEO Constellations with Gradient Staleness Mitigation0
FederatedTrust: A Solution for Trustworthy Federated Learning0
FedHAP: Federated Hashing with Global Prototypes for Cross-silo Retrieval0
Federated t-SNE and UMAP for Distributed Data Visualization0
Federated Two-stage Learning with Sign-based Voting0
Federated UCBVI: Communication-Efficient Federated Regret Minimization with Heterogeneous Agents0
Federated Unbiased Learning to Rank0
Federated Uncertainty-Aware Aggregation for Fundus Diabetic Retinopathy Staging0
Federated Uncertainty-Aware Learning for Distributed Hospital EHR Data0
Federated Unlearning0
FedCAda: Adaptive Client-Side Optimization for Accelerated and Stable Federated Learning0
Federated Unlearning for Human Activity Recognition0
A Federated Approach to Predict Emojis in Hindi Tweets0
Differentially Private Federated Combinatorial Bandits with Constraints0
Federated Unlearning Model Recovery in Data with Skewed Label Distributions0
Federated Unlearning via Active Forgetting0
Federated Learning with Uncertainty via Distilled Predictive Distributions0
FedBWO: Enhancing Communication Efficiency in Federated Learning0
Federated Unlearning with Knowledge Distillation0
Federated Unsupervised Domain Adaptation for Face Recognition0
A review of Federated Learning in Intrusion Detection Systems for IoT0
Federated Unsupervised Representation Learning0
Federated Unsupervised Semantic Segmentation0
Differentially Private Federated Learning without Noise Addition: When is it Possible?0
Federated User Representation Learning0
Federated Variational Inference for Bayesian Mixture Models0
FedGraph: an Aggregation Method from Graph Perspective0
Federated Variational Inference: Towards Improved Personalization and Generalization0
Federated Virtual Learning on Heterogeneous Data with Local-global Distillation0
Differentially Private Federated Learning: A Systematic Review0
FedBRB: An Effective Solution to the Small-to-Large Scenario in Device-Heterogeneity Federated Learning0
A Review of Federated Learning in Energy Systems0
Federated Word2Vec: Leveraging Federated Learning to Encourage Collaborative Representation Learning0
Federated XGBoost on Sample-Wise Non-IID Data0
FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models0
Exploring Semantic Attributes from A Foundation Model for Federated Learning of Disjoint Label Spaces0
FedBot: Enhancing Privacy in Chatbots with Federated Learning0
Differentially Private Federated Learning of Diffusion Models for Synthetic Tabular Data Generation0
Communication-Efficient and Personalized Federated Foundation Model Fine-Tuning via Tri-Matrix Adaptation0
FedET: A Communication-Efficient Federated Class-Incremental Learning Framework Based on Enhanced Transformer0
FeDETR: a Federated Approach for Stenosis Detection in Coronary Angiography0
FedEval: A Holistic Evaluation Framework for Federated Learning0
FedEval-LLM: Federated Evaluation of Large Language Models on Downstream Tasks with Collective Wisdom0
FedEx: Expediting Federated Learning over Heterogeneous Mobile Devices by Overlapping and Participant Selection0
ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning0
FedFa: A Fully Asynchronous Training Paradigm for Federated Learning0
FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix0
FedGraph: Federated Graph Learning with Intelligent Sampling0
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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