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 49515000 of 6771 papers

TitleStatusHype
Architecture Agnostic Federated Learning for Neural Networks0
Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoTCode1
Federated Graph Neural Networks: Overview, Techniques and Challenges0
OLIVE: Oblivious Federated Learning on Trusted Execution Environment against the risk of sparsificationCode1
Federated Learning with Sparsified Model Perturbation: Improving Accuracy under Client-Level Differential Privacy0
UA-FedRec: Untargeted Attack on Federated News RecommendationCode1
Federated Contrastive Learning for Dermatological Disease Diagnosis via On-device Learning0
FLHub: a Federated Learning model sharing service0
Do Gradient Inversion Attacks Make Federated Learning Unsafe?0
On the Convergence of Clustered Federated LearningCode0
On Federated Learning with Energy Harvesting Clients0
Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning0
SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing0
Game of Privacy: Towards Better Federated Platform Collaboration under Privacy Restriction0
PPA: Preference Profiling Attack Against Federated Learning0
Personalization Improves Privacy-Accuracy Tradeoffs in Federated LearningCode0
FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling0
FedQAS: Privacy-aware machine reading comprehension with federated learningCode0
Vertical Federated Learning: Challenges, Methodologies and Experiments0
Techtile -- Open 6G R&D Testbed for Communication, Positioning, Sensing, WPT and Federated Learning0
Identifying Backdoor Attacks in Federated Learning via Anomaly Detection0
SwiftAgg: Communication-Efficient and Dropout-Resistant Secure Aggregation for Federated Learning with Worst-Case Security Guarantees0
Federated Learning of Generative Image Priors for MRI ReconstructionCode1
APPFL: Open-Source Software Framework for Privacy-Preserving Federated LearningCode1
Learnings from Federated Learning in the Real world0
Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Data0
Fabricated Flips: Poisoning Federated Learning without Data0
Addressing modern and practical challenges in machine learning: A survey of online federated and transfer learning0
Preserving Privacy and Security in Federated Learning0
More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks0
FL_PyTorch: optimization research simulator for federated learningCode1
ABG: A Multi-Party Mixed Protocol Framework for Privacy-Preserving Cooperative Learning0
Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks0
Lossy Gradient Compression: How Much Accuracy Can One Bit Buy?Code0
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine LearningCode1
Energy-Aware Edge Association for Cluster-based Personalized Federated Learning0
Communication Efficient Federated Learning via Ordered ADMM in a Fully Decentralized Setting0
Privacy-preserving Speech Emotion Recognition through Semi-Supervised Federated LearningCode1
A Coalition Formation Game Approach for Personalized Federated Learning0
Improved Information Theoretic Generalization Bounds for Distributed and Federated Learning0
Aggregation Service for Federated Learning: An Efficient, Secure, and More Resilient Realization0
Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT0
Comparative assessment of federated and centralized machine learning0
Proportional Fairness in Federated LearningCode1
Byzantine-Robust Decentralized Learning via ClippedGossipCode1
Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoTCode0
FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stations0
DASHA: Distributed Nonconvex Optimization with Communication Compression, Optimal Oracle Complexity, and No Client Synchronization0
Federated Reinforcement Learning for Collective Navigation of Robotic Swarms0
Communication Efficient Federated Learning for Generalized Linear Bandits0
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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