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 25012550 of 6771 papers

TitleStatusHype
Bayesian data fusion with shared priors0
Device Scheduling with Fast Convergence for Wireless Federated Learning0
Device Scheduling for Relay-assisted Over-the-Air Aggregation in Federated Learning0
A Bayesian Framework for Clustered Federated Learning0
Almost Cost-Free Communication in Federated Best Arm Identification0
Adaptively Weighted Averaging Over-the-Air Computation and Its Application to Distributed Gaussian Process Regression0
ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning0
Device Scheduling for Over-the-Air Federated Learning with Differential Privacy0
Device Scheduling and Update Aggregation Policies for Asynchronous Federated Learning0
Bayesian AirComp with Sign-Alignment Precoding for Wireless Federated Learning0
Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Things0
Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation0
Bayes' capacity as a measure for reconstruction attacks in federated learning0
HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learning0
Device Sampling and Resource Optimization for Federated Learning in Cooperative Edge Networks0
BayBFed: Bayesian Backdoor Defense for Federated Learning0
De-VertiFL: A Solution for Decentralized Vertical Federated Learning0
DeTrigger: A Gradient-Centric Approach to Backdoor Attack Mitigation in Federated Learning0
Battery-aware Cyclic Scheduling in Energy-harvesting Federated Learning0
A Linearized Alternating Direction Multiplier Method for Federated Matrix Completion Problems0
Detection of ransomware attacks using federated learning based on the CNN model0
Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications0
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning0
Detection of Insider Attacks in Distributed Projected Subgradient Algorithms0
Detection of Global Anomalies on Distributed IoT Edges with Device-to-Device Communication0
Buffered Asynchronous SGD for Byzantine Learning0
Detection and Prevention Against Poisoning Attacks in Federated Learning0
Detailed comparison of communication efficiency of split learning and federated learning0
BARA: Efficient Incentive Mechanism with Online Reward Budget Allocation in Cross-Silo Federated Learning0
Aligning Beam with Imbalanced Multi-modality: A Generative Federated Learning Approach0
A Lightweight Method for Tackling Unknown Participation Statistics in Federated Averaging0
Desirable Companion for Vertical Federated Learning: New Zeroth-Order Gradient Based Algorithm0
Design of Two-Level Incentive Mechanisms for Hierarchical Federated Learning0
Bandwidth Slicing to Boost Federated Learning in Edge Computing0
Design and implementation of a distributed security threat detection system integrating federated learning and multimodal LLM0
Design and Analysis of Uplink and Downlink Communications for Federated Learning0
Bandwidth-Aware and Overlap-Weighted Compression for Communication-Efficient Federated Learning0
DESED-FL and URBAN-FL: Federated Learning Datasets for Sound Event Detection0
DER Forecast using Privacy Preserving Federated Learning0
Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks0
DepthFL: Depthwise Federated Learning for Heterogeneous Clients0
Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning0
Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning0
A Life-long Learning Intrusion Detection System for 6G-Enabled IoV0
Adaptive incentive for cross-silo federated learning: A multi-agent reinforcement learning approach0
Depersonalized Federated Learning: Tackling Statistical Heterogeneity by Alternating Stochastic Gradient Descent0
Denoising and Adaptive Online Vertical Federated Learning for Sequential Multi-Sensor Data in Industrial Internet of Things0
Banded Square Root Matrix Factorization for Differentially Private Model Training0
Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning0
Demystifying the Effects of Non-Independence in Federated Learning0
Show:102550
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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