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 51100 of 6771 papers

TitleStatusHype
FedML: A Research Library and Benchmark for Federated Machine LearningCode2
Enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare ApplicationsCode2
Efficient Federated Learning Tiny Language Models for Mobile Network Feature PredictionCode2
FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language ModelsCode2
Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation NetworkCode2
FedFMS: Exploring Federated Foundation Models for Medical Image SegmentationCode2
A Comprehensive Guide to Explainable AI: From Classical Models to LLMsCode2
A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual LearningCode2
COALA: A Practical and Vision-Centric Federated Learning PlatformCode2
Advances in APPFL: A Comprehensive and Extensible Federated Learning FrameworkCode2
Adaptive Personalized Federated LearningCode2
Adaptive Latent-Space Constraints in Personalized FLCode2
Fed3DGS: Scalable 3D Gaussian Splatting with Federated LearningCode2
CEFHRI: A Communication Efficient Federated Learning Framework for Recognizing Industrial Human-Robot InteractionCode1
Catastrophic Data Leakage in Vertical Federated LearningCode1
CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian SamplingCode1
CaPC Learning: Confidential and Private Collaborative LearningCode1
Can Textual Gradient Work in Federated Learning?Code1
CAR-MFL: Cross-Modal Augmentation by Retrieval for Multimodal Federated Learning with Missing ModalitiesCode1
Enhancing Efficiency in Multidevice Federated Learning through Data SelectionCode1
CAFE: Catastrophic Data Leakage in Vertical Federated LearningCode1
Can Foundation Models Help Us Achieve Perfect Secrecy?Code1
ByzFL: Research Framework for Robust Federated LearningCode1
Byzantine-Robust Learning on Heterogeneous Datasets via BucketingCode1
C2A: Client-Customized Adaptation for Parameter-Efficient Federated LearningCode1
CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated LearningCode1
Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated LearningCode1
Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive CollaborationCode1
Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT ImagingCode1
Masked Jigsaw Puzzle: A Versatile Position Embedding for Vision TransformersCode1
Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated Graph Neural NetworkCode1
Bias Propagation in Federated LearningCode1
Analysis and Evaluation of Synchronous and Asynchronous FLchainCode1
Byzantine-Robust Decentralized Learning via ClippedGossipCode1
A Blockchain-based Decentralized Federated Learning Framework with Committee ConsensusCode1
Benchmarking Differential Privacy and Federated Learning for BERT ModelsCode1
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of ModelsCode1
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine LearningCode1
ARFED: Attack-Resistant Federated averaging based on outlier eliminationCode1
Benchmarking Algorithms for Federated Domain GeneralizationCode1
Beyond Traditional Threats: A Persistent Backdoor Attack on Federated LearningCode1
Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy GuaranteesCode1
Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept DriftCode1
A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data SourcesCode1
An In-Depth Evaluation of Federated Learning on Biomedical Natural Language ProcessingCode1
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated LearningCode1
A Better Alternative to Error Feedback for Communication-Efficient Distributed LearningCode1
Bayesian Framework for Gradient LeakageCode1
Attack of the Tails: Yes, You Really Can Backdoor Federated LearningCode1
Asynchronous Federated Continual LearningCode1
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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