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 29012950 of 6771 papers

TitleStatusHype
Federated Knowledge Graph Unlearning via Diffusion Model0
MGIC: A Multi-Label Gradient Inversion Attack based on Canny Edge Detection on Federated Learning0
AFGI: Towards Accurate and Fast-convergent Gradient Inversion Attack in Federated Learning0
Efficient Language Model Architectures for Differentially Private Federated Learning0
Federated Learning of Socially Appropriate Agent Behaviours in Simulated Home EnvironmentsCode0
Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains0
Don't Forget What I did?: Assessing Client Contributions in Federated Learning0
DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning0
UAV-Enabled Asynchronous Federated Learning0
Adaptive Federated Learning Over the Air0
DrJAX: Scalable and Differentiable MapReduce Primitives in JAX0
FewFedPIT: Towards Privacy-preserving and Few-shot Federated Instruction Tuning0
Analysis of Total Variation Minimization for Clustered Federated Learning0
Fluent: Round-efficient Secure Aggregation for Private Federated Learning0
Fake or Compromised? Making Sense of Malicious Clients in Federated Learning0
Towards Efficient Replay in Federated Incremental Learning0
Federated Learning Method for Preserving Privacy in Face Recognition System0
RIS-empowered Topology Control for Distributed Learning in Urban Air Mobility0
FL-GUARD: A Holistic Framework for Run-Time Detection and Recovery of Negative Federated Learning0
On-demand Quantization for Green Federated Generative Diffusion in Mobile Edge Networks0
Boosting Fairness and Robustness in Over-the-Air Federated Learning0
HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated LearningCode0
FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering0
Architectural Blueprint For Heterogeneity-Resilient Federated Learning0
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression0
SPEAR:Exact Gradient Inversion of Batches in Federated Learning0
OCD-FL: A Novel Communication-Efficient Peer Selection-based Decentralized Federated Learning0
Do You Trust Your Model? Emerging Malware Threats in the Deep Learning Ecosystem0
Many-Objective Multi-Solution Transport0
Decoupled Vertical Federated Learning for Practical Training on Vertically Partitioned Data0
Enhancing Security in Federated Learning through Adaptive Consensus-Based Model Update Validation0
Leveraging Federated Learning and Edge Computing for Recommendation Systems within Cloud Computing Networks0
Training Machine Learning models at the Edge: A Survey0
Federated Learning Under Attack: Exposing Vulnerabilities through Data Poisoning Attacks in Computer NetworksCode0
Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks0
PPS-QMIX: Periodically Parameter Sharing for Accelerating Convergence of Multi-Agent Reinforcement LearningCode0
FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive ModelsCode0
Leveraging Federated Learning for Automatic Detection of Clopidogrel Treatment Failures0
Rethinking Clustered Federated Learning in NOMA Enhanced Wireless Networks0
Federated Learning over Connected ModesCode0
Towards Robust Federated Learning via Logits Calibration on Non-IID Data0
MeanCache: User-Centric Semantic Caching for LLM Web Services0
A Survey on Federated Unlearning: Challenges and Opportunities0
Towards Optimal Customized Architecture for Heterogeneous Federated Learning with Contrastive Cloud-Edge Model DecouplingCode0
Enhancing Data Provenance and Model Transparency in Federated Learning Systems - A Database Approach0
A Hierarchical Federated Learning Approach for the Internet of Things0
Partial Federated Learning0
Asyn2F: An Asynchronous Federated Learning Framework with Bidirectional Model Aggregation0
A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications0
Analysis of Privacy Leakage in Federated Large Language ModelsCode0
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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