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 13511400 of 6771 papers

TitleStatusHype
Defending Label Inference Attacks in Split Learning under Regression Setting0
Defending the Edge: Representative-Attention for Mitigating Backdoor Attacks in Federated Learning0
DeFL: Decentralized Weight Aggregation for Cross-silo Federated Learning0
Delay Analysis of Wireless Federated Learning Based on Saddle Point Approximation and Large Deviation Theory0
Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey0
Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning0
A Green Multi-Attribute Client Selection for Over-The-Air Federated Learning: A Grey-Wolf-Optimizer Approach0
Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models0
A Graph Federated Architecture with Privacy Preserving Learning0
Adaptive Federated Learning in Heterogeneous Wireless Networks with Independent Sampling0
Adaptive Federated Learning and Digital Twin for Industrial Internet of Things0
Automated Federated Learning in Mobile Edge Networks -- Fast Adaptation and Convergence0
AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning0
AGRAMPLIFIER: Defending Federated Learning Against Poisoning Attacks Through Local Update Amplification0
Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation0
Agnostic Personalized Federated Learning with Kernel Factorization0
Defending against the Label-flipping Attack in Federated Learning0
AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving0
Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation0
Adaptive Federated Dropout: Improving Communication Efficiency and Generalization for Federated Learning0
AGIC: Approximate Gradient Inversion Attack on Federated Learning0
Defending Against Poisoning Attacks in Federated Learning with Blockchain0
Defending against Poisoning Backdoor Attacks on Federated Meta-learning0
Aggregation Weighting of Federated Learning via Generalization Bound Estimation0
A Unified Learn-to-Distort-Data Framework for Privacy-Utility Trade-off in Trustworthy Federated Learning0
Mitigating Malicious Attacks in Federated Learning via Confidence-aware Defense0
A Unified Knowledge Distillation Framework for Deep Directed Graphical Models0
Acceleration for Compressed Gradient Descent in Distributed Optimization0
Defending Against Gradient Inversion Attacks for Biomedical Images via Learnable Data Perturbation0
Defending against Reconstruction Attack in Vertical Federated Learning0
Batch Label Inference and Replacement Attacks in Black-Boxed Vertical Federated Learning0
Delay-Aware Hierarchical Federated Learning0
Demo: A Practical Testbed for Decentralized Federated Learning on Physical Edge Devices0
A Unified Analysis of Stochastic Gradient Descent with Arbitrary Data Permutations and Beyond0
A Unified Analysis of Federated Learning with Arbitrary Client Participation0
Aggregation Service for Federated Learning: An Efficient, Secure, and More Resilient Realization0
AugMixCloak: A Defense against Membership Inference Attacks via Image Transformation0
Heterogeneous Federated Learning using Dynamic Model Pruning and Adaptive Gradient0
2SFGL: A Simple And Robust Protocol For Graph-Based Fraud Detection0
Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks0
Overcoming Noisy and Irrelevant Data in Federated Learning0
Data Selection for Efficient Model Update in Federated Learning0
Towards Practical Few-shot Federated NLP0
Data Reconstruction Attacks and Defenses: A Systematic Evaluation0
Data Quality Control in Federated Instruction-tuning of Large Language Models0
Aggregating Low Rank Adapters in Federated Fine-tuning0
Data privacy protection in microscopic image analysis for material data mining0
Data Poisoning Attacks on Federated Machine Learning0
Dataset Distillation-based Hybrid Federated Learning on Non-IID Data0
Auditable Homomorphic-based Decentralized Collaborative AI with Attribute-based Differential Privacy0
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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