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 67016750 of 6771 papers

TitleStatusHype
Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms0
Towards Fair and Privacy-Preserving Federated Deep ModelsCode0
Secure Distributed On-Device Learning Networks With Byzantine Adversaries0
Bayesian Nonparametric Federated Learning of Neural NetworksCode0
Fair Resource Allocation in Federated LearningCode0
Self-supervised audio representation learning for mobile devices0
Decentralized Bayesian Learning over Graphs0
Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Data0
BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning0
Fair Resource Allocation in Federated Learning0
Client-Edge-Cloud Hierarchical Federated LearningCode0
LEAF: A Benchmark for Federated Settings0
Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach0
Federated Optimization for Heterogeneous NetworksCode0
One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve ThemCode0
Robust Federated Training via Collaborative Machine Teaching using Trusted Instances0
Probabilistic Federated Neural Matching0
Semi-Cyclic Stochastic Gradient Descent0
Federated Learning Of Out-Of-Vocabulary Words0
Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregation0
Asynchronous Federated OptimizationCode0
Robust and Communication-Efficient Federated Learning from Non-IID DataCode0
One-Shot Federated Learning0
High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributionsCode0
Federated Machine Learning: Concept and ApplicationsCode0
Peer-to-peer Federated Learning on Graphs0
Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation SystemCode0
SecureBoost: A Lossless Federated Learning Framework0
Profit Allocation for Federated LearningCode0
Federated Learning via Over-the-Air Computation0
Multi-objective Evolutionary Federated Learning0
Expanding the Reach of Federated Learning by Reducing Client Resource Requirements0
Learning Private Neural Language Modeling with Attentive AggregationCode0
No Peek: A Survey of private distributed deep learning0
A Hybrid Approach to Privacy-Preserving Federated LearningCode0
Differentially Private Data Generative Models0
Protection Against Reconstruction and Its Applications in Private Federated Learning0
Split learning for health: Distributed deep learning without sharing raw patient dataCode0
Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning0
Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data0
FADL:Federated-Autonomous Deep Learning for Distributed Electronic Health Record0
Partitioned Variational Inference: A unified framework encompassing federated and continual learning0
A Survey of Mobile Computing for the Visually Impaired0
Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine LearningCode0
Dancing in the Dark: Private Multi-Party Machine Learning in an Untrusted SettingCode0
WEST: Word Encoded Sequence Transducers0
A generic framework for privacy preserving deep learningCode0
MD-GAN: Multi-Discriminator Generative Adversarial Networks for Distributed DatasetsCode0
Federated Learning for Mobile Keyboard PredictionCode0
Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data0
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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