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 51515200 of 6771 papers

TitleStatusHype
Adaptive Federated Pruning in Hierarchical Wireless Networks0
AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems0
Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach0
Adaptive Histogram-Based Gradient Boosted Trees for Federated Learning0
Adaptive Hyper-graph Aggregation for Modality-Agnostic Federated Learning0
Adaptive incentive for cross-silo federated learning: A multi-agent reinforcement learning approach0
Adaptively Weighted Averaging Over-the-Air Computation and Its Application to Distributed Gaussian Process Regression0
Adaptive Parameterization of Deep Learning Models for Federated Learning0
Adaptive Personlization in Federated Learning for Highly Non-i.i.d. Data0
Adaptive Prototype Knowledge Transfer for Federated Learning with Mixed Modalities and Heterogeneous Tasks0
Adaptive Quantization of Model Updates for Communication-Efficient Federated Learning0
Adaptive Quantization Resolution and Power Control for Federated Learning over Cell-free Networks0
Adaptive Rank Allocation for Federated Parameter-Efficient Fine-Tuning of Language Models0
Adaptive Scheduling for Machine Learning Tasks over Networks0
Adaptive Social Metaverse Streaming based on Federated Multi-Agent Deep Reinforcement Learning0
Adaptive Transmission Scheduling in Wireless Networks for Asynchronous Federated Learning0
Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoT Networks0
AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks0
Adapt to Adaptation: Learning to Personalize for Cross-Silo Federated Learning0
AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning0
A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks0
Addressing Client Drift in Federated Continual Learning with Adaptive Optimization0
Addressing Data Heterogeneity in Federated Learning of Cox Proportional Hazards Models0
Addressing Data Heterogeneity in Federated Learning with Adaptive Normalization-Free Feature Recalibration0
Addressing Heterogeneity in Federated Learning: Challenges and Solutions for a Shared Production Environment0
Addressing Heterogeneity in Federated Load Forecasting with Personalization Layers0
Addressing modern and practical challenges in machine learning: A survey of online federated and transfer learning0
Addressing Skewed Heterogeneity via Federated Prototype Rectification with Personalization0
Addressing Spatial-Temporal Data Heterogeneity in Federated Continual Learning via Tail Anchor0
A decentralized aggregation mechanism for training deep learning models using smart contract system for bank loan prediction0
Decision Models for Selecting Federated Learning Architecture Patterns0
Collaborative Distributed Machine Learning0
A Differentially Private Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations0
A distillation-based approach integrating continual learning and federated learning for pervasive services0
Towards cost-effective and resource-aware aggregation at Edge for Federated Learning0
A Distributed Approach to Meteorological Predictions: Addressing Data Imbalance in Precipitation Prediction Models through Federated Learning and GANs0
A Distributed Computation Model Based on Federated Learning Integrates Heterogeneous models and Consortium Blockchain for Solving Time-Varying Problems0
A Distributed Cubic-Regularized Newton Method for Smooth Convex Optimization over Networks0
ADMarker: A Multi-Modal Federated Learning System for Monitoring Digital Biomarkers of Alzheimer's Disease0
A(DP)^2SGD: Asynchronous Decentralized Parallel Stochastic Gradient Descent with Differential Privacy0
AdRo-FL: Informed and Secure Client Selection for Federated Learning in the Presence of Adversarial Aggregator0
Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detection0
Advanced Relay-Based Collaborative Framework for Optimizing Synchronization in Split Federated Learning over Wireless Networks0
Advancements in Federated Learning: Models, Methods, and Privacy0
Advancements of federated learning towards privacy preservation: from federated learning to split learning0
Advances and Challenges in Meta-Learning: A Technical Review0
Advances and Open Challenges in Federated Foundation Models0
Advances in Robust Federated Learning: Heterogeneity Considerations0
Advancing Deep Learning through Probability Engineering: A Pragmatic Paradigm for Modern AI0
Advancing Federated Learning in 6G: A Trusted Architecture with Graph-based Analysis0
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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