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 44514500 of 6771 papers

TitleStatusHype
FedSyn: Synthetic Data Generation using Federated Learning0
FedSynthCT-Brain: A Federated Learning Framework for Multi-Institutional Brain MRI-to-CT Synthesis0
FedSynth: Gradient Compression via Synthetic Data in Federated Learning0
FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning0
FedTDP: A Privacy-Preserving and Unified Framework for Trajectory Data Preparation via Federated Learning0
FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning0
FedTilt: Towards Multi-Level Fairness-Preserving and Robust Federated Learning0
Distributed Pruning Towards Tiny Neural Networks in Federated Learning0
FedTLU: Federated Learning with Targeted Layer Updates0
FedToken: Tokenized Incentives for Data Contribution in Federated Learning0
FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Model0
FedTrans: Efficient Federated Learning via Multi-Model Transformation0
FedTrees: A Novel Computation-Communication Efficient Federated Learning Framework Investigated in Smart Grids0
FedTriNet: A Pseudo Labeling Method with Three Players for Federated Semi-supervised Learning0
Identifying the Truth of Global Model: A Generic Solution to Defend Against Byzantine and Backdoor Attacks in Federated Learning (full version)0
FedTSA: A Cluster-based Two-Stage Aggregation Method for Model-heterogeneous Federated Learning0
FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers0
FedUD: Exploiting Unaligned Data for Cross-Platform Federated Click-Through Rate Prediction0
FedUHB: Accelerating Federated Unlearning via Polyak Heavy Ball Method0
FedUV: Uniformity and Variance for Heterogeneous Federated Learning0
FedVAE: Trajectory privacy preserving based on Federated Variational AutoEncoder0
FedVARP: Tackling the Variance Due to Partial Client Participation in Federated Learning0
FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis0
FedVeca: Federated Vectorized Averaging on Non-IID Data with Adaptive Bi-directional Global Objective0
FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models0
FedVMR: A New Federated Learning method for Video Moment Retrieval0
FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data0
FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models0
Fedward: Flexible Federated Backdoor Defense Framework with Non-IID Data0
FedWOA: A Federated Learning Model that uses the Whale Optimization Algorithm for Renewable Energy Prediction0
FedWSIDD: Federated Whole Slide Image Classification via Dataset Distillation0
FedX: Adaptive Model Decomposition and Quantization for IoT Federated Learning0
FedXGBoost: Privacy-Preserving XGBoost for Federated Learning0
FedYolo: Augmenting Federated Learning with Pretrained Transformers0
FedZeN: Towards superlinear zeroth-order federated learning via incremental Hessian estimation0
Learning to Specialize: Joint Gating-Expert Training for Adaptive MoEs in Decentralized Settings0
FedZKP: Federated Model Ownership Verification with Zero-knowledge Proof0
FedZKT: Zero-Shot Knowledge Transfer towards Resource-Constrained Federated Learning with Heterogeneous On-Device Models0
Fed-ZOE: Communication-Efficient Over-the-Air Federated Learning via Zeroth-Order Estimation0
FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning0
Federated Learning with Heterogeneous Differential Privacy0
FetchSGD: Communication-Efficient Federated Learning with Sketching0
FEVERLESS: Fast and Secure Vertical Federated Learning based on XGBoost for Decentralized Labels0
FewFedWeight: Few-shot Federated Learning Framework across Multiple NLP Tasks0
Few-Round Learning for Federated Learning0
Few-Shot Generation of Brain Tumors for Secure and Fair Data Sharing0
FGAD: Self-boosted Knowledge Distillation for An Effective Federated Graph Anomaly Detection Framework0
FGLP: A Federated Fine-Grained Location Prediction System for Mobile Users0
FheFL: Fully Homomorphic Encryption Friendly Privacy-Preserving Federated Learning with Byzantine Users0
Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devices0
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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