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 27512800 of 6771 papers

TitleStatusHype
D-Cliques: Compensating for Data Heterogeneity with Topology in Decentralized Federated Learning0
DCFL: Non-IID awareness Data Condensation aided Federated Learning0
A Unified Knowledge Distillation Framework for Deep Directed Graphical Models0
Aggregation Weighting of Federated Learning via Generalization Bound Estimation0
Adaptive Federated Dropout: Improving Communication Efficiency and Generalization for Federated Learning0
Acceleration for Compressed Gradient Descent in Distributed Optimization0
A Bargaining-based Approach for Feature Trading in Vertical Federated Learning0
Mitigating Malicious Attacks in Federated Learning via Confidence-aware Defense0
DBFed: Debiasing Federated Learning Framework based on Domain-Independent0
A Unified Analysis of Stochastic Gradient Descent with Arbitrary Data Permutations and Beyond0
Data value estimation on private gradients0
Data Valuation for Vertical Federated Learning: A Model-free and Privacy-preserving Method0
A Unified Analysis of Federated Learning with Arbitrary Client Participation0
Aggregation Service for Federated Learning: An Efficient, Secure, and More Resilient Realization0
Data Similarity-Based One-Shot Clustering for Multi-Task Hierarchical Federated Learning0
Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms0
Dataset Distillation-based Hybrid Federated Learning on Non-IID Data0
AugMixCloak: A Defense against Membership Inference Attacks via Image Transformation0
Heterogeneous Federated Learning using Dynamic Model Pruning and Adaptive Gradient0
Overcoming Noisy and Irrelevant Data in Federated Learning0
Data Selection for Efficient Model Update in Federated Learning0
Towards Practical Few-shot Federated NLP0
Data Reconstruction Attacks and Defenses: A Systematic Evaluation0
Federated Learning in NTNs: Design, Architecture and Challenges0
Data Quality Control in Federated Instruction-tuning of Large Language Models0
Aggregating Low Rank Adapters in Federated Fine-tuning0
Data privacy protection in microscopic image analysis for material data mining0
Federated Learning in Mobile Edge Computing: An Edge-Learning Perspective for Beyond 5G0
Data Poisoning Attacks on Federated Machine Learning0
Auditable Homomorphic-based Decentralized Collaborative AI with Attribute-based Differential Privacy0
Federated Learning in MIMO Satellite Broadcast System0
Federated learning in low-resource settings: A chest imaging study in Africa -- Challenges and lessons learned0
Federated Learning in IoT: a Survey from a Resource-Constrained Perspective0
Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems0
Data Overvaluation Attack and Truthful Data Valuation in Federated Learning0
Auction-Based Ex-Post-Payment Incentive Mechanism Design for Horizontal Federated Learning with Reputation and Contribution Measurement0
Adaptive Distillation for Decentralized Learning from Heterogeneous Clients0
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization0
Federated Learning in Multi-Center Critical Care Research: A Systematic Case Study using the eICU Database0
Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Review0
Federated learning in food research0
Federated Learning in Open- and Closed-Loop EMG Decoding: A Privacy and Performance Perspective0
Federated Learning in Practice: Reflections and Projections0
Federated Learning in Satellite Constellations0
Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges0
Federated Learning in Temporal Heterogeneity0
Federated Learning in the Presence of Adversarial Client Unavailability0
Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms0
Data Obfuscation through Latent Space Projection (LSP) for Privacy-Preserving AI Governance: Case Studies in Medical Diagnosis and Finance Fraud Detection0
Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data0
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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