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 63016350 of 6771 papers

TitleStatusHype
Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees0
Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation0
Distributed Online Learning with Multiple Kernels0
Distributed Online Optimization with Stochastic Agent Availability0
Distributed Optimization for Quadratic Cost Functions over Large-Scale Networks with Quantized Communication and Finite-Time Convergence0
Distributed Optimization over Block-Cyclic Data0
Distributed Quasi-Newton Method for Fair and Fast Federated Learning0
Distributed Reinforcement Learning for Privacy-Preserving Dynamic Edge Caching0
Distributed sequential federated learning0
Distributed Skellam Mechanism: a Novel Approach to Federated Learning with Differential Privacy0
Distributed Stochastic Gradient Descent with Cost-Sensitive and Strategic Agents0
Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-supervised Learning Approach0
Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms0
Distributed Trust Through the Lens of Software Architecture0
Distributed U-net model and Image Segmentation for Lung Cancer Detection0
Decentralized Unsupervised Learning of Visual Representations0
Distributionally Robust Clustered Federated Learning: A Case Study in Healthcare0
Distributionally Robust Federated Averaging0
Distributionally Robust Federated Learning with Client Drift Minimization0
Distributionally Robust Federated Learning: An ADMM Algorithm0
Distribution-Aware Mobility-Assisted Decentralized Federated Learning0
Distribution-Free Fair Federated Learning with Small Samples0
Distribution-Free Federated Learning with Conformal Predictions0
Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity0
Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions0
Diverse Client Selection for Federated Learning via Submodular Maximization0
Diversity-Driven Learning: Tackling Spurious Correlations and Data Heterogeneity in Federated Models0
Divide-and-Conquer the NAS puzzle in Resource Constrained Federated Learning Systems0
DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning Based on Constant-Overhead Linear Secret Resharing0
DMPA: Model Poisoning Attacks on Decentralized Federated Learning for Model Differences0
Does Worst-Performing Agent Lead the Pack? Analyzing Agent Dynamics in Unified Distributed SGD0
Do Gradient Inversion Attacks Make Federated Learning Unsafe?0
Domain Discrepancy Aware Distillation for Model Aggregation in Federated Learning0
Domain Generalization for Cross-Receiver Radio Frequency Fingerprint Identification0
Don't encrypt the data; just approximate the model \ Towards Secure Transaction and Fair Pricing of Training Data0
Don't Forget What I did?: Assessing Client Contributions in Federated Learning0
Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory0
Double Momentum and Error Feedback for Clipping with Fast Rates and Differential Privacy0
Coordinating Momenta for Cross-silo Federated Learning0
Do We Really Need to Design New Byzantine-robust Aggregation Rules?0
Do You Trust Your Model? Emerging Malware Threats in the Deep Learning Ecosystem0
DP^2-FedSAM: Enhancing Differentially Private Federated Learning Through Personalized Sharpness-Aware Minimization0
DP2FL: Dual Prompt Personalized Federated Learning in Foundation Models0
DP^2-NILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring0
DPBalance: Efficient and Fair Privacy Budget Scheduling for Federated Learning as a Service0
DPCOVID: Privacy-Preserving Federated Covid-19 Detection0
DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation0
On the Convergence of Differentially Private Federated Learning on Non-Lipschitz Objectives, and with Normalized Client Updates0
DPP-based Client Selection for Federated Learning with Non-IID Data0
DP-REC: Private & Communication-Efficient 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