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
Enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare ApplicationsCode2
DPAUC: Differentially Private AUC Computation in Federated LearningCode2
FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language ModelsCode2
FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated LearningCode2
FedGraph: A Research Library and Benchmark for Federated Graph LearningCode2
Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation NetworkCode2
Beyond Local Sharpness: Communication-Efficient Global Sharpness-aware Minimization for Federated LearningCode2
Federated Learning in Mobile Networks: A Comprehensive Case Study on Traffic ForecastingCode2
An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated LearningCode2
COALA: A Practical and Vision-Centric Federated Learning PlatformCode2
Efficient Federated Learning Tiny Language Models for Mobile Network Feature PredictionCode2
A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual LearningCode2
Fed3DGS: Scalable 3D Gaussian Splatting with Federated LearningCode2
An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public TransportationCode1
Analyzing Federated Learning through an Adversarial LensCode1
CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian SamplingCode1
An Efficient Approach for Cross-Silo Federated Learning to RankCode1
CEFHRI: A Communication Efficient Federated Learning Framework for Recognizing Industrial Human-Robot InteractionCode1
Enhancing Efficiency in Multidevice Federated Learning through Data SelectionCode1
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
A Hybrid Self-Supervised Learning Framework for Vertical Federated LearningCode1
An Efficient Framework for Clustered Federated LearningCode1
CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated LearningCode1
Catastrophic Data Leakage in Vertical Federated LearningCode1
Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated LearningCode1
C2A: Client-Customized Adaptation for Parameter-Efficient Federated LearningCode1
ByzFL: Research Framework for Robust Federated LearningCode1
CAFE: Catastrophic Data Leakage in Vertical Federated LearningCode1
Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy GuaranteesCode1
Byzantine-Robust Decentralized Learning via ClippedGossipCode1
Byzantine-Robust Learning on Heterogeneous Datasets via BucketingCode1
A Blockchain-based Decentralized Federated Learning Framework with Committee ConsensusCode1
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
Bias Propagation in Federated LearningCode1
Beyond Traditional Threats: A Persistent Backdoor Attack on Federated LearningCode1
Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated Graph Neural NetworkCode1
Masked Jigsaw Puzzle: A Versatile Position Embedding for Vision TransformersCode1
Can Foundation Models Help Us Achieve Perfect Secrecy?Code1
Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept DriftCode1
A federated graph neural network framework for privacy-preserving personalizationCode1
A Federated Data-Driven Evolutionary AlgorithmCode1
Omnidirectional Transfer for Quasilinear Lifelong LearningCode1
Agnostic Federated LearningCode1
A Better Alternative to Error Feedback for Communication-Efficient Distributed LearningCode1
A Framework for Energy and Carbon Footprint Analysis of Distributed and Federated Edge LearningCode1
Analysis and Evaluation of Synchronous and Asynchronous FLchainCode1
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine 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