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 37513800 of 6771 papers

TitleStatusHype
Personalized Privacy-Preserving Framework for Cross-Silo Federated LearningCode0
Advancements in Federated Learning: Models, Methods, and Privacy0
Fusion of Global and Local Knowledge for Personalized Federated LearningCode0
FedST: Secure Federated Shapelet Transformation for Time Series Classification0
Speech Privacy Leakage from Shared Gradients in Distributed Learning0
FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization AccuracyCode1
FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learning0
CADIS: Handling Cluster-skewed Non-IID Data in Federated Learning with Clustered Aggregation and Knowledge DIStilled RegularizationCode0
A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy0
Federated Learning for ASR based on Wav2vec 2.0Code1
ByzSecAgg: A Byzantine-Resistant Secure Aggregation Scheme for Federated Learning Based on Coded Computing and Vector Commitment0
Federated Gradient Matching Pursuit0
WW-FL: Secure and Private Large-Scale Federated Learning0
TAMUNA: Doubly Accelerated Distributed Optimization with Local Training, Compression, and Partial Participation0
FederatedTrust: A Solution for Trustworthy Federated Learning0
Magnitude Matters: Fixing SIGNSGD Through Magnitude-Aware Sparsification in the Presence of Data Heterogeneity0
Breaking the Communication-Privacy-Accuracy Tradeoff with f-Differential Privacy0
On Feasibility of Server-side Backdoor Attacks on Split Learning0
Delving into the Adversarial Robustness of Federated Learning0
Smoothly Giving up: Robustness for Simple Models0
Clustered Data Sharing for Non-IID Federated Learning over Wireless Networks0
Efficient Classification of SARS-CoV-2 Spike Sequences Using Federated Learning0
Online Spatio-Temporal Correlation-Based Federated Learning for Traffic Flow Forecasting0
Towards Zero-trust Security for the Metaverse0
AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving0
Multimodal Federated Learning via Contrastive Representation EnsembleCode1
Privately Customizing Prefinetuning to Better Match User Data in Federated Learning0
Welfare and Fairness Dynamics in Federated Learning: A Client Selection Perspective0
Distributed Learning in Heterogeneous Environment: federated learning with adaptive aggregation and computation reduction0
Federated Learning as a Network Effects Game0
Balancing Privacy Protection and Interpretability in Federated Learning0
FedABC: Targeting Fair Competition in Personalized Federated Learning0
Adaptive incentive for cross-silo federated learning: A multi-agent reinforcement learning approach0
A Federated Learning Benchmark for Drug-Target InteractionCode0
Tight Auditing of Differentially Private Machine Learning0
Bayesian Federated Inference for estimating Statistical Models based on Non-shared Multicenter Data sets0
Experimenting with Emerging RISC-V Systems for Decentralised Machine LearningCode0
Revisiting Weighted Aggregation in Federated Learning with Neural NetworksCode1
FedLE: Federated Learning Client Selection with Lifespan Extension for Edge IoT Networks0
Federated Learning via Indirect Server-Client Communications0
EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous DataCode0
An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learning0
Multi-Carrier NOMA-Empowered Wireless Federated Learning with Optimal Power and Bandwidth Allocation0
PerAda: Parameter-Efficient Federated Learning Personalization with Generalization GuaranteesCode1
Communication-Efficient Federated Bilevel Optimization with Local and Global Lower Level Problems0
Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading0
FedDA: Faster Framework of Local Adaptive Gradient Methods via Restarted Dual Averaging0
One-Shot Federated Conformal PredictionCode1
FilFL: Client Filtering for Optimized Client Participation in Federated LearningCode0
Byzantine-Robust Learning on Heterogeneous Data via Gradient SplittingCode1
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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