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 21012150 of 6771 papers

TitleStatusHype
A Privacy-Preserving Federated Learning Framework for Generalizable CBCT to Synthetic CT Translation in Head and Neck0
FedAvg with Fine Tuning: Local Updates Lead to Representation Learning0
FedBABU: Toward Enhanced Representation for Federated Image Classification0
FedBaF: Federated Learning Aggregation Biased by a Foundation Model0
CorBin-FL: A Differentially Private Federated Learning Mechanism using Common Randomness0
CoRAST: Towards Foundation Model-Powered Correlated Data Analysis in Resource-Constrained CPS and IoT0
A Framework for Incentivized Collaborative Learning0
CopRA: A Progressive LoRA Training Strategy0
Coordination-free Decentralised Federated Learning on Complex Networks: Overcoming Heterogeneity0
Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noise0
Adaptive Channel Sparsity for Federated Learning Under System Heterogeneity0
Coordinated Replay Sample Selection for Continual Federated Learning0
Coordinated Attacks Against Federated Learning: A Multi-Agent Reinforcement Learning Approach0
Asynchronous Federated Learning with Incentive Mechanism Based on Contract Theory0
Cooperative Hardware-Prompt Learning for Snapshot Compressive Imaging0
Cooperative Federated Learning over Ground-to-Satellite Integrated Networks: Joint Local Computation and Data Offloading0
Asynchronous Federated Learning with Bidirectional Quantized Communications and Buffered Aggregation0
A Framework for Exploring Federated Community Detection0
FedAUXfdp: Differentially Private One-Shot Federated Distillation0
Cooperative Decentralized Backdoor Attacks on Vertical Federated Learning0
Asynchronous Federated Learning with Differential Privacy for Edge Intelligence0
Supplementary File: Cooperative Gradient Coding for Semi-Decentralized Federated Learning0
Cooperation and Personalization on a Seesaw: Choice-based FL for Safe Cooperation in Wireless Networks0
Asynchronous Federated Learning for Sensor Data with Concept Drift0
Adaptive Biased User Scheduling for Heterogeneous Wireless Federate Learning Network0
Convergent Differential Privacy Analysis for General Federated Learning: the f-DP Perspective0
Convergence Visualizer of Decentralized Federated Distillation with Reduced Communication Costs0
Convergence Time Optimization for Federated Learning over Wireless Networks0
Convergence Theory of Flexible ALADIN for Distributed Optimization0
A Framework for Evaluating Gradient Leakage Attacks in Federated Learning0
Accelerating Heterogeneous Federated Learning with Closed-form Classifiers0
Boosting Gradient Leakage Attacks: Data Reconstruction in Realistic FL Settings0
FedAuxHMTL: Federated Auxiliary Hard-Parameter Sharing Multi-Task Learning for Network Edge Traffic Classification0
Convergence Rate Maximization for Split Learning-based Control of EMG Prosthetic Devices0
Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learning0
Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy0
Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes0
Edge Bias in Federated Learning and its Solution by Buffered Knowledge Distillation0
Convergence of Federated Learning over a Noisy Downlink0
Convergence of Agnostic Federated Averaging0
Asynchronous Diffusion Learning with Agent Subsampling and Local Updates0
Convergence-aware Clustered Federated Graph Learning Framework for Collaborative Inter-company Labor Market Forecasting0
Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning0
Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging0
A Framework for Double-Blind Federated Adaptation of Foundation Models0
Convergence Analysis of Sequential Split Learning on Heterogeneous Data0
Convergence Analysis of Split Federated Learning on Heterogeneous Data0
Asynchronous Decentralized Federated Learning for Collaborative Fault Diagnosis of PV Stations0
Asynchronous Collaborative Learning Across Data Silos0
Convergence Analysis of Over-the-Air FL with Compression and Power Control via Clipping0
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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