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 20512100 of 6771 papers

TitleStatusHype
FLARE: A New Federated Learning Framework with Adjustable Learning Rates over Resource-Constrained Wireless Networks0
Leverage Variational Graph Representation For Model Poisoning on Federated LearningCode0
Distributed Learning for Wi-Fi AP Load Prediction0
Dual Model Replacement:invisible Multi-target Backdoor Attack based on Federal Learning0
Poisoning Attacks on Federated Learning-based Wireless Traffic Prediction0
Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless NetworksCode0
Apodotiko: Enabling Efficient Serverless Federated Learning in Heterogeneous Environments0
Fair Concurrent Training of Multiple Models in Federated Learning0
Machine Learning Techniques for MRI Data Processing at Expanding Scale0
FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning0
FedMPQ: Secure and Communication-Efficient Federated Learning with Multi-codebook Product Quantization0
FedTrans: Efficient Federated Learning via Multi-Model Transformation0
A SER-based Device Selection Mechanism in Multi-bits Quantization Federated Learning0
PAFedFV: Personalized and Asynchronous Federated Learning for Finger Vein Recognition0
EHRFL: Federated Learning Framework for Institution-Specific Model Construction using Electronic Health RecordsCode1
Breaking the Memory Wall for Heterogeneous Federated Learning via Progressive Training0
Intelligent Agents for Auction-based Federated Learning: A Survey0
Collaborative Visual Place Recognition through Federated Learning0
Personalized Wireless Federated Learning for Large Language Models0
MultiConfederated Learning: Inclusive Non-IID Data handling with Decentralized Federated Learning0
End-to-End Verifiable Decentralized Federated LearningCode0
CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balance0
KoReA-SFL: Knowledge Replay-based Split Federated Learning Against Catastrophic Forgetting0
Towards Multi-modal Transformers in Federated LearningCode1
KDk: A Defense Mechanism Against Label Inference Attacks in Vertical Federated Learning0
One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model DiversityCode0
FedEval-LLM: Federated Evaluation of Large Language Models on Downstream Tasks with Collective Wisdom0
FedEGG: Federated Learning with Explicit Global Guidance0
FCNCP: A Coupled Nonnegative CANDECOMP/PARAFAC Decomposition Based on Federated Learning0
FedMID: A Data-Free Method for Using Intermediate Outputs as a Defense Mechanism Against Poisoning Attacks in Federated Learning0
FedFa: A Fully Asynchronous Training Paradigm for Federated Learning0
Improved Generalization Bounds for Communication Efficient Federated Learning0
A Secure and Trustworthy Network Architecture for Federated Learning Healthcare Applications0
Lightweight Unsupervised Federated Learning with Pretrained Vision Language Model0
A Federated Learning Approach to Privacy Preserving Offensive Language Identification0
FedPFT: Federated Proxy Fine-Tuning of Foundation ModelsCode1
Personalized Federated Learning via StackingCode0
Unsupervised Speaker Diarization in Distributed IoT Networks Using Federated Learning0
Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning0
Privacy-Enhanced Training-as-a-Service for On-Device Intelligence: Concept, Architectural Scheme, and Open Problems0
Confidential Federated ComputationsCode2
A Phone-based Distributed Ambient Temperature Measurement System with An Efficient Label-free Automated Training Strategy0
FEDSTR: Money-In AI-Out | A Decentralized Marketplace for Federated Learning and LLM Training on the NOSTR Protocol0
Federated Learning on Riemannian Manifolds with Differential Privacy0
Empowering Federated Learning with Implicit Gossiping: Mitigating Connection Unreliability Amidst Unknown and Arbitrary Dynamics0
Distributed Federated Learning-Based Deep Learning Model for Privacy MRI Brain Tumor Detection0
FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity0
Unsupervised Federated Optimization at the Edge: D2D-Enabled Learning without Labels0
Communication-Efficient Hybrid Federated Learning for E-health with Horizontal and Vertical Data Partitioning0
Hybrid FedGraph: An efficient hybrid federated learning algorithm using graph convolutional neural network0
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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