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 11511200 of 6771 papers

TitleStatusHype
Stochastic Communication Avoidance for Recommendation Systems0
Federated Learning with Relative Fairness0
From Federated Learning to Quantum Federated Learning for Space-Air-Ground Integrated Networks0
False Data Injection Attack Detection in Edge-based Smart Metering Networks with Federated Learning0
Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computing0
Boosting Federated Learning with FedEntOpt: Mitigating Label Skew by Entropy-Based Client Selection0
Personalized Federated Learning via Feature Distribution AdaptationCode1
Wireless Federated Learning over UAV-enabled Integrated Sensing and Communication0
C2A: Client-Customized Adaptation for Parameter-Efficient Federated LearningCode1
FedDTPT: Federated Discrete and Transferable Prompt Tuning for Black-Box Large Language Models0
Identify Backdoored Model in Federated Learning via Individual UnlearningCode1
BACSA: A Bias-Aware Client Selection Algorithm for Privacy-Preserving Federated Learning in Wireless Healthcare Networks0
AI-based traffic analysis in digital twin networks0
Why do we regularise in every iteration for imaging inverse problems?0
On Sampling Strategies for Spectral Model Sharding0
Generative AI-Powered Plugin for Robust Federated Learning in Heterogeneous IoT NetworksCode0
Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated LearningCode1
Conformalized Prediction of Post-Fault Voltage Trajectories Using Pre-trained and Finetuned Attention-Driven Neural Operators0
Federated Black-Box Adaptation for Semantic SegmentationCode0
Federated Learning for Diabetic Retinopathy Diagnosis: Enhancing Accuracy and Generalizability in Under-Resourced Regions0
Communication-Efficient Federated Learning over Wireless Channels via Gradient Sketching0
Byzantine-Robust Federated Learning: An Overview With Focus on Developing Sybil-based Attacks to Backdoor Augmented Secure Aggregation Protocols0
CopRA: A Progressive LoRA Training Strategy0
(FL)^2: Overcoming Few Labels in Federated Semi-Supervised LearningCode0
Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and AnalysisCode0
Exploring Gradient Subspaces: Addressing and Overcoming LoRA's Limitations in Federated Fine-Tuning of Large Language Models0
A Study of Secure Algorithms for Vertical Federated Learning: Take Secure Logistic Regression as an Example0
Towards Robust and Efficient Federated Low-Rank Adaptation with Heterogeneous Clients0
Federated UCBVI: Communication-Efficient Federated Regret Minimization with Heterogeneous Agents0
PARDON: Privacy-Aware and Robust Federated Domain GeneralizationCode0
Vertical Federated Learning with Missing Features During Training and InferenceCode0
Adaptive Aggregation Weights for Federated Segmentation of Pancreas MRI0
BF-Meta: Secure Blockchain-enhanced Privacy-preserving Federated Learning for Metaverse0
Online Mirror Descent for Tchebycheff Scalarization in Multi-Objective Optimization0
OPA: One-shot Private Aggregation with Single Client Interaction and its Applications to Federated Learning0
rAge-k: Communication-Efficient Federated Learning Using Age Factor0
Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-TuningCode0
Enhanced channel estimation for near-field IRS-aided multi-user MIMO system via a large deep residual network0
On Homomorphic Encryption Based Strategies for Class Imbalance in Federated Learning0
A Unified Solution to Diverse Heterogeneities in One-shot Federated LearningCode0
A Statistical Analysis of Deep Federated Learning for Intrinsically Low-dimensional DataCode0
Federated Time Series Generation on Feature and Temporally Misaligned Data0
FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model FusionCode1
Anatomical 3D Style Transfer Enabling Efficient Federated Learning with Extremely Low Communication Costs0
FedMABA: Towards Fair Federated Learning through Multi-Armed Bandits Allocation0
Privacy-Preserving Federated Learning via Dataset Distillation0
MetaTrading: An Immersion-Aware Model Trading Framework for Vehicular Metaverse Services0
Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learning0
LanFL: Differentially Private Federated Learning with Large Language Models using Synthetic Samples0
Equitable Federated Learning with Activation Clustering0
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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