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 48514900 of 6771 papers

TitleStatusHype
Incentivizing Truthful Collaboration in Heterogeneous Federated Learning0
Inclusive Data Representation in Federated Learning: A Novel Approach Integrating Textual and Visual Prompt0
Inclusive, Differentially Private Federated Learning for Clinical Data0
Incremental Semi-supervised Federated Learning for Health Inference via Mobile Sensing0
IndicFed: A Federated Approach for Sentiment Analysis in Indic Languages0
Industrial Federated Learning -- Requirements and System Design0
Industry-Scale Orchestrated Federated Learning for Drug Discovery0
In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning0
Inference-Time Personalized Federated Learning0
On-Demand Unlabeled Personalized Federated Learning0
Influence-oriented Personalized Federated Learning0
Information Stealing in Federated Learning Systems Based on Generative Adversarial Networks0
Information-Theoretic Bounds on the Generalization Error and Privacy Leakage in Federated Learning0
Information-Theoretic Generalization Analysis for Topology-aware Heterogeneous Federated Edge Learning over Noisy Channels0
Information-Theoretic Perspective of Federated Learning0
Initialisation and Network Effects in Decentralised Federated Learning0
Initialization Matters: Unraveling the Impact of Pre-Training on Federated Learning0
Input Reconstruction Attack against Vertical Federated Large Language Models0
INSIGHT: A Survey of In-Network Systems for Intelligent, High-Efficiency AI and Topology Optimization0
Instance-hiding Schemes for Private Distributed Learning0
Integrating Asynchronous AdaBoost into Federated Learning: Five Real World Applications0
Integrating Identity-Based Identification against Adaptive Adversaries in Federated Learning0
Integrating Local Real Data with Global Gradient Prototypes for Classifier Re-Balancing in Federated Long-Tailed Learning0
Integrating Personalized Federated Learning with Control Systems for Enhanced Performance0
Intelligent Agents for Auction-based Federated Learning: A Survey0
Intelligent Data-Driven Architectural Features Orchestration for Network Slicing0
Intelligent Radio Signal Processing: A Survey0
Intelligent Transportation Systems' Orchestration: Lessons Learned & Potential Opportunities0
Intelligent Travel Activity Monitoring: Generalized Distributed Acoustic Sensing Approaches0
Interaction-Aware Gaussian Weighting for Clustered Federated Learning0
Interaction-level Membership Inference Attack Against Federated Recommender Systems0
Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networks0
Intermittent Pulling with Local Compensation for Communication-Efficient Federated Learning0
Interplay between Federated Learning and Explainable Artificial Intelligence: a Scoping Review0
Interpretable collaborative data analysis on distributed data0
Interpretable Data Fusion for Distributed Learning: A Representative Approach via Gradient Matching0
Intracranial Hemorrhage Detection Using Neural Network Based Methods With Federated Learning0
Intrinisic Gradient Compression for Federated Learning0
Intrinsic Gradient Compression for Scalable and Efficient Federated Learning0
Introducing Federated Learning into Internet of Things ecosystems -- preliminary considerations0
Invariant Aggregator for Defending against Federated Backdoor Attacks0
Invariant Federated Learning for Edge Intelligence: Mitigating Heterogeneity and Asynchrony via Exit Strategy and Invariant Penalty0
Inverse Distance Aggregation for Federated Learning with Non-IID Data0
Inverse Feasibility in Over-the-Air Federated Learning0
Investigating Effective Speaker Property Privacy Protection in Federated Learning for Speech Emotion Recognition0
Investigating Neuron Disturbing in Fusing Heterogeneous Neural Networks0
IoT Federated Blockchain Learning at the Edge0
The Internet of Things in the Era of Generative AI: Vision and Challenges0
IPFed: Identity protected federated learning for user authentication0
IPMN Risk Assessment under Federated Learning Paradigm0
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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