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 22512300 of 6771 papers

TitleStatusHype
Anatomical 3D Style Transfer Enabling Efficient Federated Learning with Extremely Low Communication Costs0
Addressing Client Drift in Federated Continual Learning with Adaptive Optimization0
Achieving Linear Speedup in Non-IID Federated Bilevel Learning0
A blockchain-orchestrated Federated Learning architecture for healthcare consortia0
Accelerated Distributed Stochastic Non-Convex Optimization over Time-Varying Directed Networks0
DPBalance: Efficient and Fair Privacy Budget Scheduling for Federated Learning as a Service0
Blockchain-based Secure Client Selection in Federated Learning0
DP^2-NILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring0
DP2FL: Dual Prompt Personalized Federated Learning in Foundation Models0
Blockchain-based Optimized Client Selection and Privacy Preserved Framework for Federated Learning0
Anarchic Federated Learning0
DP^2-FedSAM: Enhancing Differentially Private Federated Learning Through Personalized Sharpness-Aware Minimization0
Do You Trust Your Model? Emerging Malware Threats in the Deep Learning Ecosystem0
Blockchain-based Monitoring for Poison Attack Detection in Decentralized Federated Learning0
Do We Really Need to Design New Byzantine-robust Aggregation Rules?0
Coordinating Momenta for Cross-silo Federated Learning0
Blockchain-based Framework for Scalable and Incentivized Federated Learning0
An Analysis of Untargeted Poisoning Attack and Defense Methods for Federated Online Learning to Rank Systems0
Double Momentum and Error Feedback for Clipping with Fast Rates and Differential Privacy0
Blockchain-based Federated Learning with Secure Aggregation in Trusted Execution Environment for Internet-of-Things0
Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory0
Don't Forget What I did?: Assessing Client Contributions in Federated Learning0
Blockchain-Based Federated Learning in Mobile Edge Networks with Application in Internet of Vehicles0
Analyzing the vulnerabilities in SplitFed Learning: Assessing the robustness against Data Poisoning Attacks0
Don't encrypt the data; just approximate the model \ Towards Secure Transaction and Fair Pricing of Training Data0
Domain Generalization for Cross-Receiver Radio Frequency Fingerprint Identification0
Blockchain-based Federated Learning for Decentralized Energy Management Systems0
Domain Discrepancy Aware Distillation for Model Aggregation in Federated Learning0
Blockchain-based Federated Learning for Failure Detection in Industrial IoT0
Analyzing the Robustness of Decentralized Horizontal and Vertical Federated Learning Architectures in a Non-IID Scenario0
A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks0
Achieving Linear Speedup in Asynchronous Federated Learning with Heterogeneous Clients0
Do Gradient Inversion Attacks Make Federated Learning Unsafe?0
Does Worst-Performing Agent Lead the Pack? Analyzing Agent Dynamics in Unified Distributed SGD0
A Systematic Survey of Blockchained Federated Learning0
Blockchain-Based Federated Learning: Incentivizing Data Sharing and Penalizing Dishonest Behavior0
Resilience in Online Federated Learning: Mitigating Model-Poisoning Attacks via Partial Sharing0
DMPA: Model Poisoning Attacks on Decentralized Federated Learning for Model Differences0
DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning Based on Constant-Overhead Linear Secret Resharing0
Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation0
Divide-and-Conquer the NAS puzzle in Resource Constrained Federated Learning Systems0
Diversity-Driven Learning: Tackling Spurious Correlations and Data Heterogeneity in Federated Models0
Blockchain Assisted Decentralized Federated Learning (BLADE-FL) with Lazy Clients0
AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning0
Diverse Client Selection for Federated Learning via Submodular Maximization0
Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensing0
Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions0
Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity0
Label Inference Attacks against Node-level Vertical Federated GNNs0
Analytic Personalized Federated Meta-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