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 31013150 of 6771 papers

TitleStatusHype
Federated Classification in Hyperbolic Spaces via Secure Aggregation of Convex HullsCode0
Data-Efficient Energy-Aware Participant Selection for UAV-Enabled Federated Learning0
FedEdge AI-TC: A Semi-supervised Traffic Classification Method based on Trusted Federated Deep Learning for Mobile Edge Computing0
Approximate and Weighted Data Reconstruction Attack in Federated Learning0
SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models0
Performance Analysis for Resource Constrained Decentralized Federated Learning Over Wireless Networks0
Towards Instance-adaptive Inference for Federated LearningCode1
UFed-GAN: A Secure Federated Learning Framework with Constrained Computation and Unlabeled Data0
FLShield: A Validation Based Federated Learning Framework to Defend Against Poisoning Attacks0
A Comprehensive Empirical Study of Bugs in Open-Source Federated Learning Frameworks0
GIFD: A Generative Gradient Inversion Method with Feature Domain OptimizationCode1
Tram-FL: Routing-based Model Training for Decentralized Federated Learning0
Wirelessly Powered Federated Learning Networks: Joint Power Transfer, Data Sensing, Model Training, and Resource Allocation0
Feature Matching Data Synthesis for Non-IID Federated Learning0
Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate GradientsCode0
Backdoor Federated Learning by Poisoning Backdoor-Critical Layers0
ConDistFL: Conditional Distillation for Federated Learning from Partially Annotated DataCode2
A Survey on Decentralized Federated Learning0
Pelta: Shielding Transformers to Mitigate Evasion Attacks in Federated Learning0
The Prospect of Enhancing Large-Scale Heterogeneous Federated Learning with Transformers0
FLIPS: Federated Learning using Intelligent Participant Selection0
A Reinforcement Learning-Based Approach to Graph Discovery in D2D-Enabled Federated Learning0
When Federated Learning meets Watermarking: A Comprehensive Overview of Techniques for Intellectual Property Protection0
Binary Federated Learning with Client-Level Differential Privacy0
The Copycat Perceptron: Smashing Barriers Through Collective Learning0
Cross-Silo Prototypical Calibration for Federated Learning with Non-IID DataCode1
Adapter-based Selective Knowledge Distillation for Federated Multi-domain Meeting Summarization0
Communication-Efficient Framework for Distributed Image Semantic Wireless Transmission0
Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data MiningCode0
Private Federated Learning with Dynamic Power Control via Non-Coherent Over-the-Air Computation0
Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity0
SureFED: Robust Federated Learning via Uncertainty-Aware Inward and Outward Inspection0
Label Inference Attacks against Node-level Vertical Federated GNNs0
Scaling Survival Analysis in Healthcare with Federated Survival Forests: A Comparative Study on Heart Failure and Breast Cancer Genomics0
Federated Learning: Organizational Opportunities, Challenges, and Adoption Strategies0
Federated Representation Learning for Automatic Speech Recognition0
Hierarchical Federated Learning in Wireless Networks: Pruning Tackles Bandwidth Scarcity and System Heterogeneity0
Dynamic Privacy Allocation for Locally Differentially Private Federated Learning with Composite Objectives0
Compressed and distributed least-squares regression: convergence rates with applications to Federated Learning0
Revolutionizing Wireless Networks with Federated Learning: A Comprehensive Review0
Differential Privacy for Adaptive Weight Aggregation in Federated Tumor Segmentation0
Physics-Driven Spectrum-Consistent Federated Learning for Palmprint VerificationCode1
Data Collaboration Analysis applied to Compound Datasets and the Introduction of Projection data to Non-IID settings0
Asynchronous Federated Learning with Bidirectional Quantized Communications and Buffered Aggregation0
AQUILA: Communication Efficient Federated Learning with Adaptive Quantization in Device Selection Strategy0
Federated Learning for Data and Model Heterogeneity in Medical Imaging0
Efficient Federated Learning via Local Adaptive Amended Optimizer with Linear Speedup0
Proof-of-Federated-Learning-Subchain: Free Partner Selection Subchain Based on Federated Learning0
Shuffled Differentially Private Federated Learning for Time Series Data Analytics0
SemiSFL: Split Federated Learning on Unlabeled and Non-IID DataCode0
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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