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 55515600 of 6771 papers

TitleStatusHype
Towards Practical Few-shot Federated NLP0
AugMixCloak: A Defense against Membership Inference Attacks via Image Transformation0
A Unified Analysis of Federated Learning with Arbitrary Client Participation0
A Unified Analysis of Stochastic Gradient Descent with Arbitrary Data Permutations and Beyond0
A Unified Knowledge Distillation Framework for Deep Directed Graphical Models0
A Unified Learn-to-Distort-Data Framework for Privacy-Utility Trade-off in Trustworthy Federated Learning0
Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation0
AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving0
Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation0
AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning0
Automated Federated Learning in Mobile Edge Networks -- Fast Adaptation and Convergence0
Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models0
Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning0
Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey0
Automatic Tuning of Federated Learning Hyper-Parameters from System Perspective0
AutoRank: MCDA Based Rank Personalization for LoRA-Enabled Distributed Learning0
Auxo: Efficient Federated Learning via Scalable Client Clustering0
AVDDPG: Federated reinforcement learning applied to autonomous platoon control0
A Vertical Federated Learning Framework for Horizontally Partitioned Labels0
A Vertical Federated Learning Framework for Graph Convolutional Network0
A Vertical Federated Learning Method for Interpretable Scorecard and Its Application in Credit Scoring0
A Vertical Federated Learning Method For Multi-Institutional Credit Scoring: MICS0
Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations0
Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data0
A Web-Based Solution for Federated Learning with LLM-Based Automation0
A Weighted Loss Approach to Robust Federated Learning under Data Heterogeneity0
B^2SFL: A Bi-level Blockchained Architecture for Secure Federated Learning-based Traffic Prediction0
Information-Geometric Barycenters for Bayesian Federated Learning0
Backdoor Attack and Defense in Federated Generative Adversarial Network-based Medical Image Synthesis0
Backdoor Attack on Vertical Federated Graph Neural Network Learning0
Backdoor attacks and defenses in feature-partitioned collaborative learning0
Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directions0
Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient Ensembling0
Backdoor Attacks in Peer-to-Peer Federated Learning0
Backdoor Attacks on Federated Meta-Learning0
Backdoor Defense in Federated Learning Using Differential Testing and Outlier Detection0
Backdoor Federated Learning by Poisoning Backdoor-Critical Layers0
BACSA: A Bias-Aware Client Selection Algorithm for Privacy-Preserving Federated Learning in Wireless Healthcare Networks0
Bad-PFL: Exploring Backdoor Attacks against Personalized Federated Learning0
BadSampler: Harnessing the Power of Catastrophic Forgetting to Poison Byzantine-robust Federated Learning0
BadSFL: Backdoor Attack against Scaffold Federated Learning0
BadVFL: Backdoor Attacks in Vertical Federated Learning0
BaFFLe: Backdoor detection via Feedback-based Federated Learning0
BAFFLE: TOWARDS RESOLVING FEDERATED LEARNING’S DILEMMA - THWARTING BACKDOOR AND INFERENCE ATTACKS0
Balanced Multi-modal Federated Learning via Cross-Modal Infiltration0
Balancing Accuracy and Integrity for Reconfigurable Intelligent Surface-aided Over-the-Air Federated Learning0
Balancing Accuracy and Training Time in Federated Learning for Violence Detection in Surveillance Videos: A Study of Neural Network Architectures0
Balancing Client Participation in Federated Learning Using AoI0
Balancing Energy Efficiency and Distributional Robustness in Over-the-Air Federated Learning0
FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation0
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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