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 12511300 of 6771 papers

TitleStatusHype
Bandwidth Slicing to Boost Federated Learning in Edge Computing0
Bandwidth-Aware and Overlap-Weighted Compression for Communication-Efficient Federated Learning0
Adaptive incentive for cross-silo federated learning: A multi-agent reinforcement learning approach0
Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks0
Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning0
A Life-long Learning Intrusion Detection System for 6G-Enabled IoV0
Accurate and Fast Federated Learning via IID and Communication-Aware Grouping0
Banded Square Root Matrix Factorization for Differentially Private Model Training0
Balancing Similarity and Complementarity for Federated Learning0
Algorithm Fairness in AI for Medicine and Healthcare0
Balancing Security and Accuracy: A Novel Federated Learning Approach for Cyberattack Detection in Blockchain Networks0
Balancing Privacy Protection and Interpretability in Federated Learning0
Declarative Privacy-Preserving Inference Queries0
Adaptive Hyper-graph Aggregation for Modality-Agnostic Federated Learning0
ABC-FL: Anomalous and Benign client Classification in Federated Learning0
Decentralised and collaborative machine learning framework for IoT0
Decentralised Traffic Incident Detection via Network Lasso0
Decentralized EM to Learn Gaussian Mixtures from Datasets Distributed by Features0
Decentralized Federated Learning via Mutual Knowledge Transfer0
Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks0
Privacy-Preserving Taxi-Demand Prediction Using Federated Learning0
FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation0
A-LAQ: Adaptive Lazily Aggregated Quantized Gradient0
Balancing Energy Efficiency and Distributional Robustness in Over-the-Air Federated Learning0
Balancing Client Participation in Federated Learning Using AoI0
A Knowledge Distillation-Based Backdoor Attack in Federated Learning0
Adaptive Histogram-Based Gradient Boosted Trees for Federated Learning0
Balancing Accuracy and Training Time in Federated Learning for Violence Detection in Surveillance Videos: A Study of Neural Network Architectures0
Balancing Accuracy and Integrity for Reconfigurable Intelligent Surface-aided Over-the-Air Federated Learning0
Accurate and Fast Federated Learning via Combinatorial Multi-Armed Bandits0
Balanced Multi-modal Federated Learning via Cross-Modal Infiltration0
BAFFLE: TOWARDS RESOLVING FEDERATED LEARNING’S DILEMMA - THWARTING BACKDOOR AND INFERENCE ATTACKS0
A Joint Gradient and Loss Based Clustered Federated Learning Design0
Federated Learning Architectures: A Performance Evaluation with Crop Yield Prediction Application0
Dealing Doubt: Unveiling Threat Models in Gradient Inversion Attacks under Federated Learning, A Survey and Taxonomy0
Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach0
BaFFLe: Backdoor detection via Feedback-based Federated Learning0
A Bayesian Incentive Mechanism for Poison-Resilient Federated Learning0
BadVFL: Backdoor Attacks in Vertical Federated Learning0
BadSFL: Backdoor Attack against Scaffold Federated Learning0
AI Security for Geoscience and Remote Sensing: Challenges and Future Trends0
D-Cliques: Compensating for Data Heterogeneity with Topology in Decentralized Federated Learning0
BadSampler: Harnessing the Power of Catastrophic Forgetting to Poison Byzantine-robust Federated Learning0
Bad-PFL: Exploring Backdoor Attacks against Personalized Federated Learning0
AirTOWN: A Privacy-Preserving Mobile App for Real-time Pollution-Aware POI Suggestion0
BACSA: A Bias-Aware Client Selection Algorithm for Privacy-Preserving Federated Learning in Wireless Healthcare Networks0
AirFL-Mem: Improving Communication-Learning Trade-Off by Long-Term Memory0
Accuracy-Privacy Trade-off in the Mitigation of Membership Inference Attack in Federated Learning0
DEAL: Decremental Energy-Aware Learning in a Federated System0
DearFSAC: An Approach to Optimizing Unreliable Federated Learning via Deep Reinforcement Learning0
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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