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 30013050 of 6771 papers

TitleStatusHype
Federated Bayesian Network Ensembles0
Secure Federated Learning Across Heterogeneous Cloud and High-Performance Computing Resources -- A Case Study on Federated Fine-tuning of LLaMA 20
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
Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection0
Enhancing Convergence in Federated Learning: A Contribution-Aware Asynchronous Approach0
FedD2S: Personalized Data-Free Federated Knowledge Distillation0
FedLion: Faster Adaptive Federated Optimization with Fewer CommunicationCode0
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
Investigation of Federated Learning Algorithms for Retinal Optical Coherence Tomography Image Classification with Statistical HeterogeneityCode0
SusFL: Energy-Aware Federated Learning-based Monitoring for Sustainable Smart Farms0
Adaptive Federated Learning in Heterogeneous Wireless Networks with Independent Sampling0
Some Targets Are Harder to Identify than Others: Quantifying the Target-dependent Membership Leakage0
FedRDF: A Robust and Dynamic Aggregation Function against Poisoning Attacks in Federated Learning0
Smart Information Exchange for Unsupervised Federated Learning via Reinforcement Learning0
Federated Prompt-based Decision Transformer for Customized VR Services in Mobile Edge Computing System0
A Federated Framework for LLM-based RecommendationCode0
FedAnchor: Enhancing Federated Semi-Supervised Learning with Label Contrastive Loss for Unlabeled Clients0
A privacy-preserving, distributed and cooperative FCM-based learning approach for cancer research0
Momentum Approximation in Asynchronous Private Federated Learning0
FedSiKD: Clients Similarity and Knowledge Distillation: Addressing Non-i.i.d. and Constraints in Federated LearningCode0
Exploring Federated Deep Learning for Standardising Naming Conventions in Radiotherapy Data0
Scheduling for On-Board Federated Learning with Satellite Clusters0
FLASH: Federated Learning Across Simultaneous Heterogeneities0
Data Reconstruction Attacks and Defenses: A Systematic Evaluation0
Empowering Federated Learning for Massive Models with NVIDIA FLARE0
Bayesian Deep Learning Via Expectation Maximization and Turbo Deep Approximate Message Passing0
Differentially Private Decentralized Learning with Random WalksCode0
Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated LearningCode0
Hypernetwork-Driven Model Fusion for Federated Domain Generalization0
FedImpro: Measuring and Improving Client Update in Federated Learning0
Version age-based client scheduling policy for federated learning0
Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices0
Flashback: Understanding and Mitigating Forgetting in Federated Learning0
On the Convergence of Zeroth-Order Federated Tuning for Large Language Models0
Asynchronous Diffusion Learning with Agent Subsampling and Local Updates0
Revisiting Early-Learning Regularization When Federated Learning Meets Noisy Labels0
Examining Modality Incongruity in Multimodal Federated Learning for Medical Vision and Language-based Disease Detection0
Blockchain-enabled Clustered and Scalable Federated Learning (BCS-FL) Framework in UAV Networks0
Personalized Federated Learning for Statistical Heterogeneity0
The Role of LLMs in Sustainable Smart Cities: Applications, Challenges, and Future Directions0
Federated Learning Can Find Friends That Are Advantageous0
Expediting In-Network Federated Learning by Voting-Based Consensus Model Compression0
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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