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 62016250 of 6771 papers

TitleStatusHype
Design and implementation of a distributed security threat detection system integrating federated learning and multimodal LLM0
Design of Two-Level Incentive Mechanisms for Hierarchical Federated Learning0
Desirable Companion for Vertical Federated Learning: New Zeroth-Order Gradient Based Algorithm0
Detailed comparison of communication efficiency of split learning and federated learning0
Detection and Prevention Against Poisoning Attacks in Federated Learning0
Detection of Global Anomalies on Distributed IoT Edges with Device-to-Device Communication0
Detection of Insider Attacks in Distributed Projected Subgradient Algorithms0
Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications0
Detection of ransomware attacks using federated learning based on the CNN model0
DeTrigger: A Gradient-Centric Approach to Backdoor Attack Mitigation in Federated Learning0
De-VertiFL: A Solution for Decentralized Vertical Federated Learning0
Device Sampling and Resource Optimization for Federated Learning in Cooperative Edge Networks0
Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation0
Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Things0
Device Scheduling and Update Aggregation Policies for Asynchronous Federated Learning0
Device Scheduling for Over-the-Air Federated Learning with Differential Privacy0
Device Scheduling for Relay-assisted Over-the-Air Aggregation in Federated Learning0
Device Scheduling with Fast Convergence for Wireless Federated Learning0
DFDG: Data-Free Dual-Generator Adversarial Distillation for One-Shot Federated Learning0
DFedADMM: Dual Constraints Controlled Model Inconsistency for Decentralized Federated Learning0
dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis0
DFML: Decentralized Federated Mutual Learning0
DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning0
DID-eFed: Facilitating Federated Learning as a Service with Decentralized Identities0
Differentially Private AUC Computation in Vertical Federated Learning0
Differentially Private CutMix for Split Learning with Vision Transformer0
Differentially Private Data Generative Models0
Differentially Private Distributed Convex Optimization0
Differentially Private Federated Combinatorial Bandits with Constraints0
Differentially Private Federated Bayesian Optimization with Distributed Exploration0
Differentially Private Federated Learning with Local Regularization and Sparsification0
Differentially Private Federated Learning: Servers Trustworthiness, Estimation, and Statistical Inference0
Differentially Private Federated Learning without Noise Addition: When is it Possible?0
Differentially Private Federated Learning With Time-Adaptive Privacy Spending0
Differentially Private Federated Learning: A Systematic Review0
Differentially Private Federated Learning for Resource-Constrained Internet of Things0
Differentially Private Federated Learning of Diffusion Models for Synthetic Tabular Data Generation0
Differentially Private Federated Learning via Inexact ADMM0
Differentially Private Federated Learning via Inexact ADMM with Multiple Local Updates0
Differentially Private Low-Rank Adaptation of Large Language Model Using Federated Learning0
Differentially Private Meta-Learning0
Differentially Private Multi-Site Treatment Effect Estimation0
Differentially Private Online Federated Learning with Correlated Noise0
Differentially Private Over-the-Air Federated Learning Over MIMO Fading Channels0
Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applications0
Differentially Private Vertical Federated Learning0
Differentially Private Wireless Federated Learning Using Orthogonal Sequences0
Differential Privacy-Driven Framework for Enhancing Heart Disease Prediction0
Differential Privacy-enabled Federated Learning for Sensitive Health Data0
Differential Privacy for Adaptive Weight Aggregation in Federated Tumor Segmentation0
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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