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 23012350 of 6771 papers

TitleStatusHype
CoDream: Exchanging dreams instead of models for federated aggregation with heterogeneous models0
Bayesian Neural Network For Personalized Federated Learning Parameter Selection0
ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devices0
FedMM: Federated Multi-Modal Learning with Modality Heterogeneity in Computational Pathology0
Chu-ko-nu: A Reliable, Efficient, and Anonymously Authentication-Enabled Realization for Multi-Round Secure Aggregation in Federated Learning0
Enhancing One-Shot Federated Learning Through Data and Ensemble Co-BoostingCode1
A Bargaining-based Approach for Feature Trading in Vertical Federated Learning0
Convergence Analysis of Split Federated Learning on Heterogeneous Data0
RVE-PFL: Robust Variational Encoder-based Personalised Federated Learning against Model Inversion AttacksCode0
Efficient Unbiased Sparsification0
Practical Insights into Knowledge Distillation for Pre-Trained Models0
Privacy-Enhancing Collaborative Information Sharing through Federated Learning -- A Case of the Insurance Industry0
Federated Fairness without Access to Sensitive Groups0
Federated Learning in Genetics: Extended Analysis of Accuracy, Performance and Privacy Trade-offs0
Federated Neural Graph Databases0
FedADMM-InSa: An Inexact and Self-Adaptive ADMM for Federated Learning0
Random Aggregate Beamforming for Over-the-Air Federated Learning in Large-Scale Networks0
FGAD: Self-boosted Knowledge Distillation for An Effective Federated Graph Anomaly Detection Framework0
Federated Multi-Task Learning on Non-IID Data Silos: An Experimental StudyCode0
Stochastic Approximation Approach to Federated Machine Learning0
Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates0
ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised LearningCode0
Secure Federated Learning Across Heterogeneous Cloud and High-Performance Computing Resources -- A Case Study on Federated Fine-tuning of LLaMA 20
Federated Bayesian Network Ensembles0
On the Byzantine-Resilience of Distillation-Based Federated LearningCode0
Poisoning Federated Recommender Systems with Fake Users0
Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources0
Achieving Linear Speedup in Asynchronous Federated Learning with Heterogeneous Clients0
Enhancing Convergence in Federated Learning: A Contribution-Aware Asynchronous Approach0
Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection0
FedKit: Enabling Cross-Platform Federated Learning for Android and iOSCode1
FedD2S: Personalized Data-Free Federated Knowledge Distillation0
SusFL: Energy-Aware Federated Learning-based Monitoring for Sustainable Smart Farms0
FedRDF: A Robust and Dynamic Aggregation Function against Poisoning Attacks in Federated Learning0
Federated Prompt-based Decision Transformer for Customized VR Services in Mobile Edge Computing System0
Prompt-based Personalized Federated Learning for Medical Visual Question Answering0
DPBalance: Efficient and Fair Privacy Budget Scheduling for Federated Learning as a Service0
An advanced data fabric architecture leveraging homomorphic encryption and federated learning0
Digital versus Analog Transmissions for Federated Learning over Wireless Networks0
Benchmarking federated strategies in Peer-to-Peer Federated learning for biomedical data0
A chaotic maps-based privacy-preserving distributed deep learning for incomplete and Non-IID datasets0
Adaptive Federated Learning in Heterogeneous Wireless Networks with Independent Sampling0
Smart Information Exchange for Unsupervised Federated Learning via Reinforcement Learning0
Investigation of Federated Learning Algorithms for Retinal Optical Coherence Tomography Image Classification with Statistical HeterogeneityCode0
FedAnchor: Enhancing Federated Semi-Supervised Learning with Label Contrastive Loss for Unlabeled Clients0
A Federated Framework for LLM-based RecommendationCode0
Some Targets Are Harder to Identify than Others: Quantifying the Target-dependent Membership Leakage0
A privacy-preserving, distributed and cooperative FCM-based learning approach for cancer research0
FedLion: Faster Adaptive Federated Optimization with Fewer CommunicationCode0
FedSiKD: Clients Similarity and Knowledge Distillation: Addressing Non-i.i.d. and Constraints in Federated LearningCode0
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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