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 30013050 of 6771 papers

TitleStatusHype
Federated Low-Rank Tensor Estimation for Multimodal Image Reconstruction0
Deep Transfer Hashing for Adaptive Learning on Federated Streaming Data0
Federated Semi-supervised Learning for Medical Image Segmentation with intra-client and inter-client Consistency0
Federated X-armed Bandit with Flexible Personalisation0
Federated Meta Learning Enhanced Acoustic Radio Cooperative Framework for Ocean of Things Underwater Acoustic Communications0
Federated Meta-Learning for Few-Shot Fault Diagnosis with Representation Encoding0
FedCC: Robust Federated Learning against Model Poisoning Attacks0
A Federated Channel Modeling System using Generative Neural Networks0
Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan0
Communication-Efficient Decentralized Learning with Sparsification and Adaptive Peer Selection0
FedCCL: Federated Dual-Clustered Feature Contrast Under Domain Heterogeneity0
A review on different techniques used to combat the non-IID and heterogeneous nature of data in FL0
Federated Model Distillation with Noise-Free Differential Privacy0
Federated Model Heterogeneous Matryoshka Representation Learning0
FedCCEA : A Practical Approach of Client Contribution Evaluation for Federated Learning0
Bad-PFL: Exploring Backdoor Attacks against Personalized Federated Learning0
Federated Multi-Agent Actor-Critic Learning for Age Sensitive Mobile Edge Computing0
Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management0
Federated Multi-Agent Mapping for Planetary Exploration0
Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction0
Federated Multi-Armed Bandits Under Byzantine Attacks0
Defending against Poisoning Backdoor Attacks on Federated Meta-learning0
Communication-Efficient Consensus Mechanism for Federated Reinforcement Learning0
Accelerating Energy-Efficient Federated Learning in Cell-Free Networks with Adaptive Quantization0
Federated Multi-Mini-Batch: An Efficient Training Approach to Federated Learning in Non-IID Environments0
BadSFL: Backdoor Attack against Scaffold Federated Learning0
Federated Multi-Objective Learning0
Federated Semi-Supervised Learning with Class Distribution Mismatch0
Federated Multiple Label Hashing (FedMLH): Communication Efficient Federated Learning on Extreme Classification Tasks0
Federated Multi-Target Domain Adaptation0
Batch Label Inference and Replacement Attacks in Black-Boxed Vertical Federated Learning0
FedCau: A Proactive Stop Policy for Communication and Computation Efficient Federated Learning0
Defending Label Inference Attacks in Split Learning under Regression Setting0
Communication Efficient ConFederated Learning: An Event-Triggered SAGA Approach0
Federated Multi-View Learning for Private Medical Data Integration and Analysis0
Federated Multi-view Matrix Factorization for Personalized Recommendations0
Federated Multi-View Synthesizing for Metaverse0
FedCAT: Towards Accurate Federated Learning via Device Concatenation0
Communication-Efficient Byzantine-Resilient Federated Zero-Order Optimization0
A Review of Privacy-preserving Federated Learning for the Internet-of-Things0
Federated Nearest Neighbor Machine Translation0
Federated Neural Architecture Search with Model-Agnostic Meta Learning0
Communication-Efficient and Tensorized Federated Fine-Tuning of Large Language Models0
Federated Neural Compression Under Heterogeneous Data0
DeFTA: A Plug-and-Play Decentralized Replacement for FedAvg0
FedCanon: Non-Convex Composite Federated Learning with Efficient Proximal Operation on Heterogeneous Data0
FederatedNILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring based on Federated Deep Learning0
A review of federated learning in renewable energy applications: Potential, challenges, and future directions0
Delay-Aware Hierarchical Federated Learning0
FedCALM: Conflict-aware Layer-wise Mitigation for Selective Aggregation in Deeper Personalized Federated 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