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 53515400 of 6771 papers

TitleStatusHype
Two-Bit Aggregation for Communication Efficient and Differentially Private Federated Learning0
Efficient and Private Federated Learning with Partially Trainable Networks0
FedTune: Automatic Tuning of Federated Learning Hyper-Parameters from System PerspectiveCode0
Federated Learning via Plurality VoteCode0
Secure Byzantine-Robust Distributed Learning via Clustering0
SSFL: Tackling Label Deficiency in Federated Learning via Personalized Self-Supervision0
A Systematic Survey of Blockchained Federated Learning0
Secure Aggregation for Buffered Asynchronous Federated Learning0
Federating for Learning Group Fair Models0
Communication-Efficient Federated Learning with Binary Neural NetworksCode1
FedDQ: Communication-Efficient Federated Learning with Descending Quantization0
Privacy enabled Financial Text Classification using Differential Privacy and Federated Learning0
Distributed Learning Approaches for Automated Chest X-Ray Diagnosis0
Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits0
DESTRESS: Computation-Optimal and Communication-Efficient Decentralized Nonconvex Finite-Sum OptimizationCode1
SecFL: Confidential Federated Learning using TEEs0
FairFed: Enabling Group Fairness in Federated Learning0
Lightweight Transformer in Federated Setting for Human Activity Recognition0
Personalized Retrogress-Resilient Framework for Real-World Medical Federated LearningCode1
Algorithm Fairness in AI for Medicine and Healthcare0
Layer-wise and Dimension-wise Locally Adaptive Federated Learning0
Federated Learning in ASR: Not as Easy as You ThinkCode0
Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devices0
Coding for Straggler Mitigation in Federated Learning0
FedMorph: Communication Efficient Federated Learning via Morphing Neural Network0
A Unified Knowledge Distillation Framework for Deep Directed Graphical Models0
EP-GAN: Unsupervised Federated Learning with Expectation-Propagation Prior GAN0
ZeroSARAH: Efficient Nonconvex Finite-Sum Optimization with Zero Full Gradient Computations0
Federated Learning with Data-Agnostic Distribution Fusion0
Automatic Tuning of Federated Learning Hyper-Parameters from System Perspective0
FLBoost: On-the-Fly Fine-tuning Boosts Federated Learning via Data-free Distillation0
Personalized Heterogeneous Federated Learning with Gradient Similarity0
Personalized Neural Architecture Search for Federated Learning0
Agnostic Personalized Federated Learning with Kernel Factorization0
Predictive Maintenance for Optical Networks in Robust Collaborative Learning0
On Heterogeneously Distributed Data, Sparsity Matters0
Accelerating Federated Split Learning via Local-Loss-Based Training0
Adversarial Collaborative Learning on Non-IID Features0
Federated Inference through Aligning Local Representations and Learning a Consensus Graph0
-Weighted Federated Adversarial Training0
Positive and Unlabeled Federated Learning0
FEVERLESS: Fast and Secure Vertical Federated Learning based on XGBoost for Decentralized Labels0
Provable Federated Adversarial Learning via Min-max Optimization0
Bit-aware Randomized Response for Local Differential Privacy in Federated Learning0
Demystifying Hyperparameter Optimization in Federated Learning0
Inference-Time Personalized Federated Learning0
Diverse Client Selection for Federated Learning via Submodular Maximization0
FedNAS: Federated Deep Learning via Neural Architecture Search0
Rethinking Client Reweighting for Selfish Federated Learning0
Iterative Sketching and its Application to Federated Learning0
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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