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 26012650 of 6771 papers

TitleStatusHype
Adaptive Gradient Clipping for Robust Federated Learning0
Variational Bayes for Federated Continual LearningCode0
Distributed Continual Learning0
Worldwide Federated Training of Language Models0
Federated Online Adaptation for Deep Stereo0
Federated Domain-Specific Knowledge Transfer on Large Language Models Using Synthetic Data0
Towards Privacy-Aware and Personalised Assistive Robots: A User-Centred Approach0
CG-FedLLM: How to Compress Gradients in Federated Fune-tuning for Large Language Models0
Emulating Full Participation: An Effective and Fair Client Selection Strategy for Federated Learning0
Diffusion-Based Cloud-Edge-Device Collaborative Learning for Next POI Recommendations0
Banded Square Root Matrix Factorization for Differentially Private Model Training0
Data-Free Federated Class Incremental Learning with Diffusion-Based Generative Memory0
Task-agnostic Decision Transformer for Multi-type Agent Control with Federated Split Training0
AdaFedFR: Federated Face Recognition with Adaptive Inter-Class Representation Learning0
Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Review0
Rehearsal-free Federated Domain-incremental Learning0
Fair Evaluation of Federated Learning Algorithms for Automated Breast Density Classification: The Results of the 2022 ACR-NCI-NVIDIA Federated Learning Challenge0
FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?Code0
Communication-Efficient Federated Learning via Clipped Uniform QuantizationCode0
Marginal and training-conditional guarantees in one-shot federated conformal predictionCode0
Decentralized Federated Learning Over Imperfect Communication Channels0
Maverick-Aware Shapley Valuation for Client Selection in Federated Learning0
FedASTA: Federated adaptive spatial-temporal attention for traffic flow prediction0
Exploring and Exploiting the Asymmetric Valley of Deep Neural Networks0
Federated Learning for Time-Series Healthcare Sensing with Incomplete ModalitiesCode0
Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov Optimization Approach0
StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systems0
Continual Deep Reinforcement Learning for Decentralized Satellite Routing0
Vertical Federated Learning Hybrid Local Pre-training0
Fed-Credit: Robust Federated Learning with Credibility Management0
FedCAda: Adaptive Client-Side Optimization for Accelerated and Stable Federated Learning0
Securing Health Data on the Blockchain: A Differential Privacy and Federated Learning Framework0
The Future of Large Language Model Pre-training is Federated0
Cyclical Weight Consolidation: Towards Solving Catastrophic Forgetting in Serial Federated Learning0
Federated Learning With Energy Harvesting Devices: An MDP Framework0
Advances in Robust Federated Learning: Heterogeneity Considerations0
The Effect of Quantization in Federated Learning: A Rényi Differential Privacy Perspective0
Balancing Similarity and Complementarity for Federated Learning0
Federated Hybrid Model Pruning through Loss Landscape ExplorationCode0
Dealing Doubt: Unveiling Threat Models in Gradient Inversion Attacks under Federated Learning, A Survey and Taxonomy0
Federated Learning for Misbehaviour Detection with Variational Autoencoders and Gaussian Mixture ModelsCode0
Asynchronous Federated Stochastic Optimization for Heterogeneous Objectives Under Arbitrary Delays0
Dual-Segment Clustering Strategy for Hierarchical Federated Learning in Heterogeneous Wireless Environments0
Unmasking Efficiency: Learning Salient Sparse Models in Non-IID Federated Learning0
Real-World Federated Learning in Radiology: Hurdles to overcome and Benefits to gain0
SPD-CFL: Stepwise Parameter Dropout for Efficient Continual Federated Learning0
Byzantine-Resilient Secure Aggregation for Federated Learning Without Privacy Compromises0
Differentially Private Federated Learning: A Systematic Review0
Structured Reinforcement Learning for Incentivized Stochastic Covert Optimization0
Mitigating federated learning contribution allocation instability through randomized aggregation0
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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