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 21012150 of 6771 papers

TitleStatusHype
An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning0
Efficient and Private Federated Learning with Partially Trainable Networks0
Efficient and Privacy Preserving Group Signature for Federated Learning0
Bristle: Decentralized Federated Learning in Byzantine, Non-i.i.d. Environments0
Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout0
MUDGUARD: Taming Malicious Majorities in Federated Learning using Privacy-Preserving Byzantine-Robust Clustering0
An Energy and Carbon Footprint Analysis of Distributed and Federated Learning0
A Client-level Assessment of Collaborative Backdoor Poisoning in Non-IID Federated Learning0
Abnormal Local Clustering in Federated Learning0
Efficient Adaptive Federated Optimization of Federated Learning for IoT0
Efficient Adaptive Federated Optimization0
Bridging the Gap Between Foundation Models and Heterogeneous Federated Learning0
EFFGAN: Ensembles of fine-tuned federated GANs0
Effect of Homomorphic Encryption on the Performance of Training Federated Learning Generative Adversarial Networks0
An End-to-End Encrypted Neural Network for Gradient Updates Transmission in Federated Learning0
Effectively Heterogeneous Federated Learning: A Pairing and Split Learning Based Approach0
Bridging Data Islands: Geographic Heterogeneity-Aware Federated Learning for Collaborative Remote Sensing Semantic Segmentation0
Effective Federated Adaptive Gradient Methods with Non-IID Decentralized Data0
Bridging Data Barriers among Participants: Assessing the Potential of Geoenergy through Federated Learning0
An Empirical Study of Vulnerability Detection using Federated Learning0
A decentralized aggregation mechanism for training deep learning models using smart contract system for bank loan prediction0
Effective and secure federated online learning to rank0
Effective and Efficient Federated Tree Learning on Hybrid Data0
BRFL: A Blockchain-based Byzantine-Robust Federated Learning Model0
Effective and Efficient Cross-City Traffic Knowledge Transfer: A Privacy-Preserving Perspective0
eFedLLM: Efficient LLM Inference Based on Federated Learning0
Breaking the Memory Wall for Heterogeneous Federated Learning via Model Splitting0
An Empirical Study of the Impact of Federated Learning on Machine Learning Model Accuracy0
eFedDNN: Ensemble based Federated Deep Neural Networks for Trajectory Mode Inference0
Edge Selection and Clustering for Federated Learning in Optical Inter-LEO Satellite Constellation0
Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges0
EdgeML: Towards Network-Accelerated Federated Learning over Wireless Edge0
Breaking the Memory Wall for Heterogeneous Federated Learning via Progressive Training0
Addressing Spatial-Temporal Data Heterogeneity in Federated Continual Learning via Tail Anchor0
A Non-parametric View of FedAvg and FedProx: Beyond Stationary Points0
Edge Intelligence Over the Air: Two Faces of Interference in Federated Learning0
EdgeFL: A Lightweight Decentralized Federated Learning Framework0
Breaking the centralized barrier for cross-device federated learning0
EdgeConvEns: Convolutional Ensemble Learning for Edge Intelligence0
Breaking Secure Aggregation: Label Leakage from Aggregated Gradients in Federated Learning0
An Empirical Study of Federated Prompt Learning for Vision Language Model0
Edge-cloud Collaborative Learning with Federated and Centralized Features0
Edge-assisted U-Shaped Split Federated Learning with Privacy-preserving for Internet of Things0
Breaking Data Silos: Towards Open and Scalable Mobility Foundation Models via Generative Continual Learning0
Edge-assisted Democratized Learning Towards Federated Analytics0
An Empirical Study of Federated Learning on IoT-Edge Devices: Resource Allocation and Heterogeneity0
Addressing Skewed Heterogeneity via Federated Prototype Rectification with Personalization0
Edge-AI for Agriculture: Lightweight Vision Models for Disease Detection in Resource-Limited Settings0
EdgeAgentX: A Novel Framework for Agentic AI at the Edge in Military Communication Networks0
Brave: Byzantine-Resilient and Privacy-Preserving Peer-to-Peer 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