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 201250 of 6771 papers

TitleStatusHype
ARFED: Attack-Resistant Federated averaging based on outlier eliminationCode1
Proportional Fairness in Federated LearningCode1
Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order OptimizationCode1
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine LearningCode1
A Blockchain-based Decentralized Federated Learning Framework with Committee ConsensusCode1
Adaptive Test-Time Personalization for Federated LearningCode1
Benchmarking Differential Privacy and Federated Learning for BERT ModelsCode1
Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated LearningCode1
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of ModelsCode1
Adapt to Adaptation: Learning Personalization for Cross-Silo Federated LearningCode1
DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge DevicesCode1
Beyond Traditional Threats: A Persistent Backdoor Attack on Federated LearningCode1
Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated Graph Neural NetworkCode1
TARGET: Federated Class-Continual Learning via Exemplar-Free DistillationCode1
Analysis and Evaluation of Synchronous and Asynchronous FLchainCode1
Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT ImagingCode1
Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive CollaborationCode1
Addressing Algorithmic Disparity and Performance Inconsistency in Federated LearningCode1
Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and ChallengesCode1
Fast-Convergent Federated Learning via Cyclic AggregationCode1
Fast Federated Learning in the Presence of Arbitrary Device UnavailabilityCode1
Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew ResilienceCode1
An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public TransportationCode1
An Efficient Approach for Cross-Silo Federated Learning to RankCode1
C2A: Client-Customized Adaptation for Parameter-Efficient Federated LearningCode1
ByzFL: Research Framework for Robust Federated LearningCode1
Continual Local Training for Better Initialization of Federated ModelsCode1
CAFE: Catastrophic Data Leakage in Vertical Federated LearningCode1
Analyzing Federated Learning through an Adversarial LensCode1
FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation NoiseCode1
CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated LearningCode1
FedAS: Bridging Inconsistency in Personalized Federated LearningCode1
Can Textual Gradient Work in Federated Learning?Code1
CaPC Learning: Confidential and Private Collaborative LearningCode1
CAR-MFL: Cross-Modal Augmentation by Retrieval for Multimodal Federated Learning with Missing ModalitiesCode1
Catastrophic Data Leakage in Vertical Federated LearningCode1
CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian SamplingCode1
Enhancing Efficiency in Multidevice Federated Learning through Data SelectionCode1
A Distributed Trust Framework for Privacy-Preserving Machine LearningCode1
Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated LearningCode1
Active Membership Inference Attack under Local Differential Privacy in Federated LearningCode1
Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept DriftCode1
An Efficient Framework for Clustered Federated LearningCode1
Client-Level Differential Privacy via Adaptive Intermediary in Federated Medical ImagingCode1
Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-offCode1
FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural NetworksCode1
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated LearningCode1
CoDeC: Communication-Efficient Decentralized Continual LearningCode1
FedCoin: A Peer-to-Peer Payment System for Federated LearningCode1
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated 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