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 101150 of 6771 papers

TitleStatusHype
FedNano: Toward Lightweight Federated Tuning for Pretrained Multimodal Large Language Models0
Intelligent Travel Activity Monitoring: Generalized Distributed Acoustic Sensing Approaches0
Private Aggregation for Byzantine-Resilient Heterogeneous Federated Learning0
Wavelet Scattering Transform and Fourier Representation for Offline Detection of Malicious Clients in Federated Learning0
A Weighted Loss Approach to Robust Federated Learning under Data Heterogeneity0
SyncFed: Time-Aware Federated Learning through Explicit Timestamping and Synchronization0
FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models0
Load-Aware Training Scheduling for Model Circulation-based Decentralized Federated Learning0
A Survey on the Role of Artificial Intelligence and Machine Learning in 6G-V2X Applications0
A new type of federated clustering: A non-model-sharing approach0
Integrating Asynchronous AdaBoost into Federated Learning: Five Real World Applications0
FLoRIST: Singular Value Thresholding for Efficient and Accurate Federated Fine-Tuning of Large Language Models0
A Privacy-Preserving Federated Learning Framework for Generalizable CBCT to Synthetic CT Translation in Head and Neck0
Boosting Gradient Leakage Attacks: Data Reconstruction in Realistic FL Settings0
HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems0
FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learning0
TimberStrike: Dataset Reconstruction Attack Revealing Privacy Leakage in Federated Tree-Based SystemsCode0
Synesthesia of Machines (SoM)-Aided Online FDD Precoding via Heterogeneous Multi-Modal Sensing: A Vertical Federated Learning Approach0
Clustered Federated Learning via Embedding DistributionsCode0
Realistic Urban Traffic Generator using Decentralized Federated Learning for the SUMO simulatorCode0
Federated Learning on Stochastic Neural Networks0
UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous DataCode0
FedGA-Tree: Federated Decision Tree using Genetic Algorithm0
Latency Optimization for Wireless Federated Learning in Multihop NetworksCode0
pFedSOP : Accelerating Training Of Personalized Federated Learning Using Second-Order Optimization0
Translating Federated Learning Algorithms in Python into CSP Processes Using ChatGPT0
Mobility-Aware Asynchronous Federated Learning with Dynamic Sparsification0
Fuse and Federate: Enhancing EV Charging Station Security with Multimodal Fusion and Federated Learning0
Breaking Data Silos: Towards Open and Scalable Mobility Foundation Models via Generative Continual Learning0
Multi-Modal Multi-Task Federated Foundation Models for Next-Generation Extended Reality Systems: Towards Privacy-Preserving Distributed Intelligence in AR/VR/MR0
Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity0
Simple Yet Effective: Extracting Private Data Across Clients in Federated Fine-Tuning of Large Language Models0
Ravan: Multi-Head Low-Rank Adaptation for Federated Fine-Tuning0
FedAPM: Federated Learning via ADMM with Partial Model PersonalizationCode0
Communication Efficient Adaptive Model-Driven Quantum Federated Learning0
Federated Isolation Forest for Efficient Anomaly Detection on Edge IoT Systems0
QA-HFL: Quality-Aware Hierarchical Federated Learning for Resource-Constrained Mobile Devices with Heterogeneous Image Quality0
Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning0
FedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learning0
GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS0
Model Splitting Enhanced Communication-Efficient Federated Learning for CSI Feedback0
Gradient Inversion Attacks on Parameter-Efficient Fine-TuningCode0
HtFLlib: A Comprehensive Heterogeneous Federated Learning Library and BenchmarkCode3
Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity0
FORLA:Federated Object-centric Representation Learning with Slot Attention0
Sociodynamics-inspired Adaptive Coalition and Client Selection in Federated Learning0
FlowerTune: A Cross-Domain Benchmark for Federated Fine-Tuning of Large Language Models0
Privacy-Preserving Federated Convex Optimization: Balancing Partial-Participation and Efficiency via Noise Cancellation0
Computation- and Communication-Efficient Online FL for Resource-Constrained Aerial Vehicles0
Overcoming Challenges of Partial Client Participation in Federated Learning : A Comprehensive Review0
Show:102550
← PrevPage 3 of 136Next →

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