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 52515300 of 6771 papers

TitleStatusHype
Network Anomaly Detection Using Federated Learning and Transfer Learning0
Network Anomaly Detection Using Federated Learning0
Network EM Algorithm for Gaussian Mixture Model in Decentralized Federated Learning0
Network-GIANT: Fully distributed Newton-type optimization via harmonic Hessian consensus0
Network Gradient Descent Algorithm for Decentralized Federated Learning0
Network Structures as an Attack Surface: Topology-Based Privacy Leakage in Federated Learning0
Neural Collapse based Deep Supervised Federated Learning for Signal Detection in OFDM Systems0
Neural Collapse Inspired Federated Learning with Non-iid Data0
Neural Tangent Kernel Empowered Federated Learning0
NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA0
New Directions in Distributed Deep Learning: Bringing the Network at Forefront of IoT Design0
New Insights on Unfolding and Fine-tuning Quantum Federated Learning0
New Metrics to Evaluate the Performance and Fairness of Personalized Federated Learning0
No Free Lunch Theorem for Security and Utility in Federated Learning0
Noise Resilient Over-The-Air Federated Learning In Heterogeneous Wireless Networks0
Noise-Robust and Resource-Efficient ADMM-based Federated Learning0
Non-Coherent Over-the-Air Decentralized Gradient Descent0
Non-convex composite federated learning with heterogeneous data0
Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data0
Non-Convex Optimization in Federated Learning via Variance Reduction and Adaptive Learning0
Non-Cooperative Backdoor Attacks in Federated Learning: A New Threat Landscape0
Non-Federated Multi-Task Split Learning for Heterogeneous Sources0
Non-IID data and Continual Learning processes in Federated Learning: A long road ahead0
Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions0
Hypernetwork-Driven Model Fusion for Federated Domain Generalization0
No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices0
No Peek: A Survey of private distributed deep learning0
No prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation0
Not All Edges are Equally Robust: Evaluating the Robustness of Ranking-Based Federated Learning0
Not All Federated Learning Algorithms Are Created Equal: A Performance Evaluation Study0
NoT: Federated Unlearning via Weight Negation0
No Vandalism: Privacy-Preserving and Byzantine-Robust Federated Learning0
Now It Sounds Like You: Learning Personalized Vocabulary On Device0
NTK-DFL: Enhancing Decentralized Federated Learning in Heterogeneous Settings via Neural Tangent Kernel0
OASIS: Offsetting Active Reconstruction Attacks in Federated Learning0
OCD-FL: A Novel Communication-Efficient Peer Selection-based Decentralized Federated Learning0
OCTOPUS: Overcoming Performance andPrivatization Bottlenecks in Distributed Learning0
OFDMA-F^2L: Federated Learning With Flexible Aggregation Over an OFDMA Air Interface0
Tighter Regret Analysis and Optimization of Online Federated Learning0
OledFL: Unleashing the Potential of Decentralized Federated Learning via Opposite Lookahead Enhancement0
Olive Branch Learning: A Topology-Aware Federated Learning Framework for Space-Air-Ground Integrated Network0
On Addressing Heterogeneity in Federated Learning for Autonomous Vehicles Connected to a Drone Orchestrator0
On-Board Federated Learning for Dense LEO Constellations0
On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond0
On Data Efficiency of Meta-learning0
On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarios0
ON-DEMAND-FL: A Dynamic and Efficient Multi-Criteria Federated Learning Client Deployment Scheme0
On-Demand Model and Client Deployment in Federated Learning with Deep Reinforcement Learning0
On-demand Quantization for Green Federated Generative Diffusion in Mobile Edge Networks0
On-device Federated Learning in Smartphones for Detecting Depression from Reddit Posts0
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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