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 23512400 of 6771 papers

TitleStatusHype
FedSiKD: Clients Similarity and Knowledge Distillation: Addressing Non-i.i.d. and Constraints in Federated LearningCode0
Scheduling for On-Board Federated Learning with Satellite Clusters0
Exploring Federated Deep Learning for Standardising Naming Conventions in Radiotherapy Data0
Data Reconstruction Attacks and Defenses: A Systematic Evaluation0
FLASH: Federated Learning Across Simultaneous Heterogeneities0
FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter SharingCode1
Empowering Federated Learning for Massive Models with NVIDIA FLARE0
Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated LearningCode0
Bayesian Deep Learning Via Expectation Maximization and Turbo Deep Approximate Message Passing0
Differentially Private Decentralized Learning with Random WalksCode0
Training Heterogeneous Client Models using Knowledge Distillation in Serverless Federated LearningCode1
OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated LearningCode3
FedImpro: Measuring and Improving Client Update in Federated Learning0
Hypernetwork-Driven Model Fusion for Federated Domain Generalization0
Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-offCode1
FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated LearningCode1
Flashback: Understanding and Mitigating Forgetting in Federated Learning0
FedAA: A Reinforcement Learning Perspective on Adaptive Aggregation for Fair and Robust Federated LearningCode1
Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices0
Asynchronous Diffusion Learning with Agent Subsampling and Local Updates0
On the Convergence of Zeroth-Order Federated Tuning for Large Language Models0
Revisiting Early-Learning Regularization When Federated Learning Meets Noisy Labels0
Version age-based client scheduling policy for federated learning0
The Role of LLMs in Sustainable Smart Cities: Applications, Challenges, and Future Directions0
Personalized Federated Learning for Statistical Heterogeneity0
Blockchain-enabled Clustered and Scalable Federated Learning (BCS-FL) Framework in UAV Networks0
Examining Modality Incongruity in Multimodal Federated Learning for Medical Vision and Language-based Disease Detection0
Federated Learning Can Find Friends That Are Advantageous0
Group Distributionally Robust Dataset Distillation with Risk MinimizationCode1
Towards Fair, Robust and Efficient Client Contribution Evaluation in Federated Learning0
Decentralized Blockchain-based Robust Multi-agent Multi-armed Bandit0
Expediting In-Network Federated Learning by Voting-Based Consensus Model Compression0
Resource-Aware Hierarchical Federated Learning in Wireless Video Caching Networks0
A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective0
Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes0
Federated Learning Priorities Under the European Union Artificial Intelligence Act0
Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence GuaranteesCode0
Fairness and Privacy Guarantees in Federated Contextual Bandits0
Time-Distributed Backdoor Attacks on Federated Spiking Learning0
Exploring Federated Self-Supervised Learning for General Purpose Audio Understanding0
Towards Eliminating Hard Label Constraints in Gradient Inversion AttacksCode1
Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Things0
Rethinking the Starting Point: Collaborative Pre-Training for Federated Downstream Tasks0
Federated Learning with Differential Privacy0
Federated Learning with New Knowledge: Fundamentals, Advances, and FuturesCode2
Position Paper: Assessing Robustness, Privacy, and Fairness in Federated Learning Integrated with Foundation Models0
An Auction-based Marketplace for Model Trading in Federated Learning0
Parametric Feature Transfer: One-shot Federated Learning with Foundation Models0
DFML: Decentralized Federated Mutual Learning0
Training-time Neuron Alignment through Permutation Subspace for Improving Linear Mode Connectivity and Model Fusion0
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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