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
Communication-Efficient Robust Federated Learning with Noisy Labels0
MammoFL: Mammographic Breast Density Estimation using Federated Learning0
Federated Learning with Research Prototypes for Multi-Center MRI-based Detection of Prostate Cancer with Diverse Histopathology0
Rethinking the Defense Against Free-rider Attack From the Perspective of Model Weight Evolving FrequencyCode0
Fast Deep Autoencoder for Federated learning0
Deep Leakage from Model in Federated Learning0
On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond0
Hierarchical Federated Learning with Privacy0
Accelerating Asynchronous Federated Learning Convergence via Opportunistic Mobile Relaying0
HideNseek: Federated Lottery Ticket via Server-side Pruning and Sign Supermask0
On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data0
Leveraging Centric Data Federated Learning Using Blockchain For Integrity Assurance0
pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning0
Gradient Obfuscation Gives a False Sense of Security in Federated Learning0
FedHPO-B: A Benchmark Suite for Federated Hyperparameter Optimization0
An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning0
Subject Granular Differential Privacy in Federated Learning0
Subject Membership Inference Attacks in Federated Learning0
Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation0
FedPop: A Bayesian Approach for Personalised Federated Learning0
FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial LearningCode0
A Benchmark for Federated Hetero-Task Learning0
Group privacy for personalized federated learning0
FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning0
Certified Robustness in Federated LearningCode0
An Optimal Transport Approach to Personalized Federated Learning0
Generalized Federated Learning via Sharpness Aware MinimizationCode0
FedNST: Federated Noisy Student Training for Automatic Speech Recognition0
Rate-Distortion Theoretic Bounds on Generalization Error for Distributed LearningCode0
Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networks0
(Im)possibility of Collective Intelligence0
Straggler-Resilient Personalized Federated LearningCode0
Federated Adversarial Training with Transformers0
Sharper Rates and Flexible Framework for Nonconvex SGD with Client and Data SamplingCode0
Distributed Machine Learning in D2D-Enabled Heterogeneous Networks: Architectures, Performance, and Open Challenges0
UAV-Aided Multi-Community Federated Learning0
Scheduling for Ground-Assisted Federated Learning in LEO Satellite Constellations0
Supernet Training for Federated Image Classification under System HeterogeneityCode0
Completion Time Minimization of Fog-RAN-Assisted Federated Learning With Rate-Splitting Transmission0
Towards Group Learning: Distributed Weighting of Experts0
On the Generalization of Wasserstein Robust Federated Learning0
Federated Learning with a Sampling Algorithm under Isoperimetry0
HEX: Human-in-the-loop Explainability via Deep Reinforcement Learning0
Resource Allocation for Compression-aided Federated Learning with High Distortion Rate0
Applied Federated Learning: Architectural Design for Robust and Efficient Learning in Privacy Aware Settings0
Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the TopCode0
Walk for Learning: A Random Walk Approach for Federated Learning from Heterogeneous Data0
Defense Against Gradient Leakage Attacks via Learning to Obscure Data0
Federated Learning in Satellite Constellations0
Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic DataCode0
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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