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 701750 of 6771 papers

TitleStatusHype
DROP: Poison Dilution via Knowledge Distillation for Federated LearningCode0
Secure Visual Data Processing via Federated Learning0
Federated Learning with Reservoir State Analysis for Time Series Anomaly DetectionCode0
Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated LearningCode0
Principles and Components of Federated Learning Architectures0
Using Federated Machine Learning in Predictive Maintenance of Jet Engines0
DMPA: Model Poisoning Attacks on Decentralized Federated Learning for Model Differences0
Graph Federated Learning Based Proactive Content Caching in Edge Computing0
Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning0
Mitigating Unintended Memorization with LoRA in Federated Learning for LLMsCode1
Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks0
LATTEO: A Framework to Support Learning Asynchronously Tempered with Trusted Execution and Obfuscation0
Generative Autoregressive Transformers for Model-Agnostic Federated MRI ReconstructionCode1
Adaptive Prototype Knowledge Transfer for Federated Learning with Mixed Modalities and Heterogeneous Tasks0
SoK: Benchmarking Poisoning Attacks and Defenses in Federated LearningCode2
Private Federated Learning In Real World Application -- A Case Study0
Comparing privacy notions for protection against reconstruction attacks in machine learning0
Non-convex composite federated learning with heterogeneous data0
Interaction-Aware Gaussian Weighting for Clustered Federated Learning0
The Other Side of the Coin: Unveiling the Downsides of Model Aggregation in Federated Learning from a Layer-peeled Perspective0
FedP^2EFT: Federated Learning to Personalize Parameter Efficient Fine-Tuning for Multilingual LLMs0
Multi-objective methods in Federated Learning: A survey and taxonomy0
E-3SFC: Communication-Efficient Federated Learning with Double-way Features SynthesizingCode0
Vertical Federated Learning for Failure-Cause Identification in Disaggregated Microwave Networks0
A Hybrid Swarm Intelligence Approach for Optimizing Multimodal Large Language Models Deployment in Edge-Cloud-based Federated Learning Environments0
Gradient Correction in Federated Learning with Adaptive Optimization0
Federated Low-Rank Tensor Estimation for Multimodal Image Reconstruction0
Decoding FL Defenses: Systemization, Pitfalls, and Remedies0
Federated Learning with Discriminative Naive Bayes Classifier0
Metric Privacy in Federated Learning for Medical Imaging: Improving Convergence and Preventing Client Inference Attacks0
A Framework for Double-Blind Federated Adaptation of Foundation Models0
FedGES: A Federated Learning Approach for BN Structure Learning0
A Privacy-Preserving Domain Adversarial Federated learning for multi-site brain functional connectivity analysis0
Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework0
FedRIR: Rethinking Information Representation in Federated LearningCode0
Data Overvaluation Attack and Truthful Data Valuation in Federated Learning0
PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated LearningCode1
Robust Knowledge Distillation in Federated Learning: Counteracting Backdoor AttacksCode0
Physics-Inspired Distributed Radio Map EstimationCode1
Understanding Federated Learning from IID to Non-IID dataset: An Experimental Study0
Privacy Preserving Charge Location Prediction for Electric Vehicles0
BICompFL: Stochastic Federated Learning with Bi-Directional Compression0
Byzantine-Resilient Zero-Order Optimization for Communication-Efficient Heterogeneous Federated Learning0
Continuous-Time Analysis of Federated Averaging0
FL-APU: A Software Architecture to Ease Practical Implementation of Cross-Silo Federated Learning0
S-VOTE: Similarity-based Voting for Client Selection in Decentralized Federated Learning0
FedRTS: Federated Robust Pruning via Combinatorial Thompson SamplingCode0
SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning0
Targeted Data Fusion for Causal Survival Analysis Under Distribution Shift0
Exploring Potential Prompt Injection Attacks in Federated Military LLMs and Their Mitigation0
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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