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 60016050 of 6771 papers

TitleStatusHype
Convergence Theory of Flexible ALADIN for Distributed Optimization0
Convergence Time Optimization for Federated Learning over Wireless Networks0
Convergence Visualizer of Decentralized Federated Distillation with Reduced Communication Costs0
Convergent Differential Privacy Analysis for General Federated Learning: the f-DP Perspective0
Cooperation and Personalization on a Seesaw: Choice-based FL for Safe Cooperation in Wireless Networks0
Supplementary File: Cooperative Gradient Coding for Semi-Decentralized Federated Learning0
Cooperative Decentralized Backdoor Attacks on Vertical Federated Learning0
Cooperative Federated Learning over Ground-to-Satellite Integrated Networks: Joint Local Computation and Data Offloading0
Cooperative Hardware-Prompt Learning for Snapshot Compressive Imaging0
Coordinated Attacks Against Federated Learning: A Multi-Agent Reinforcement Learning Approach0
Coordinated Replay Sample Selection for Continual Federated Learning0
Coordination-free Decentralised Federated Learning on Complex Networks: Overcoming Heterogeneity0
CopRA: A Progressive LoRA Training Strategy0
CoRAST: Towards Foundation Model-Powered Correlated Data Analysis in Resource-Constrained CPS and IoT0
CorBin-FL: A Differentially Private Federated Learning Mechanism using Common Randomness0
Corrected with the Latest Version: Make Robust Asynchronous Federated Learning Possible0
Correlated Privacy Mechanisms for Differentially Private Distributed Mean Estimation0
Correlation Aware Sparsified Mean Estimation Using Random Projection0
CosSGD: Communication-Efficient Federated Learning with a Simple Cosine-Based Quantization0
Cost-Effective Federated Learning Design0
Cost-Effective Federated Learning in Mobile Edge Networks0
Turning Federated Learning Systems Into Covert Channels0
Covert Communication Based on the Poisoning Attack in Federated Learning0
Covert Model Poisoning Against Federated Learning: Algorithm Design and Optimization0
COVID-19 Imaging Data Privacy by Federated Learning Design: A Theoretical Framework0
Concentrated Differentially Private and Utility Preserving Federated Learning0
Critical Learning Periods in Federated Learning0
Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer0
Cross-Cloud Data Privacy Protection: Optimizing Collaborative Mechanisms of AI Systems by Integrating Federated Learning and LLMs0
Cross-device Federated Learning for Mobile Health Diagnostics: A First Study on COVID-19 Detection0
Cross-Domain Federated Learning in Medical Imaging0
Cross-domain Federated Object Detection0
Cross-Fusion Rule for Personalized Federated Learning0
Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives0
Digital Over-the-Air Federated Learning in Multi-Antenna Systems0
Cross-Modal Prototype based Multimodal Federated Learning under Severely Missing Modality0
Cross-Modal Vertical Federated Learning for MRI Reconstruction0
Cross-Silo Federated Learning: Challenges and Opportunities0
Cross-Silo Federated Learning for Multi-Tier Networks with Vertical and Horizontal Data Partitioning0
Cross-Training with Multi-View Knowledge Fusion for Heterogenous Federated Learning0
CRSFL: Cluster-based Resource-aware Split Federated Learning for Continuous Authentication0
CRS-FL: Conditional Random Sampling for Communication-Efficient and Privacy-Preserving Federated Learning0
CSAFL: A Clustered Semi-Asynchronous Federated Learning Framework0
Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning0
Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning0
Cybersecurity Threats in Connected and Automated Vehicles based Federated Learning Systems0
CYCle: Choosing Your Collaborators Wisely to Enhance Collaborative Fairness in Decentralized Learning0
Cyclical Weight Consolidation: Towards Solving Catastrophic Forgetting in Serial Federated Learning0
CyclicFL: A Cyclic Model Pre-Training Approach to Efficient Federated Learning0
D2p-fed:Differentially Private Federated Learning with Efficient Communication0
Show:102550
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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