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 45514600 of 6771 papers

TitleStatusHype
FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning0
FLMarket: Enabling Privacy-preserved Pre-training Data Pricing for Federated Learning0
FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Convergence Analysis0
Floating-floating point: a highly accurate number representation with flexible Counting ranges0
FLoBC: A Decentralized Blockchain-Based Federated Learning Framework0
FLock: Defending Malicious Behaviors in Federated Learning with Blockchain0
FLOP: Federated Learning on Medical Datasets using Partial Networks0
FLoRA: Single-shot Hyper-parameter Optimization for Federated Learning0
FLoRIST: Singular Value Thresholding for Efficient and Accurate Federated Fine-Tuning of Large Language Models0
FlowerTune: A Cross-Domain Benchmark for Federated Fine-Tuning of Large Language Models0
Flow-FL: Data-Driven Federated Learning for Spatio-Temporal Predictions in Multi-Robot Systems0
FLRA: A Reference Architecture for Federated Learning Systems0
FLShield: A Validation Based Federated Learning Framework to Defend Against Poisoning Attacks0
FLSTRA: Federated Learning in Stratosphere0
FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps0
FL-TAC: Enhanced Fine-Tuning in Federated Learning via Low-Rank, Task-Specific Adapter Clustering0
FLTG: Byzantine-Robust Federated Learning via Angle-Based Defense and Non-IID-Aware Weighting0
FLTrojan: Privacy Leakage Attacks against Federated Language Models Through Selective Weight Tampering0
FLUE: Federated Learning with Un-Encrypted model weights0
Fluent: Round-efficient Secure Aggregation for Private Federated Learning0
Fluid Antenna Enabled Over-the-Air Federated Learning: Joint Optimization of Positioning, Beamforming, and User Selection0
Fluid Democracy in Federated Data Aggregation0
FLVoogd: Robust And Privacy Preserving Federated Learning0
Collaborative Multi-source Domain Adaptation Through Optimal Transport0
FMLFS: A Federated Multi-Label Feature Selection Based on Information Theory in IoT Environment0
FMore: An Incentive Scheme of Multi-dimensional Auction for Federated Learning in MEC0
FOCUS: Dealing with Label Quality Disparity in Federated Learning0
FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data0
Foreseeing Reconstruction Quality of Gradient Inversion: An Optimization Perspective0
Forget-SVGD: Particle-Based Bayesian Federated Unlearning0
Forgetting Through Transforming: Enabling Federated Unlearning via Class-Aware Representation Transformation0
FORLA:Federated Object-centric Representation Learning with Slot Attention0
Formal Logic Enabled Personalized Federated Learning Through Property Inference0
Formal Logic-guided Robust Federated Learning against Poisoning Attacks0
Fortifying Federated Learning Towards Trustworthiness via Auditable Data Valuation and Verifiable Client Contribution0
FPGA-Based Hardware Accelerator of Homomorphic Encryption for Efficient Federated Learning0
Fractional Order Distributed Optimization0
Framework Construction of an Adversarial Federated Transfer Learning Classifier0
FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learning0
F-RBA: A Federated Learning-based Framework for Risk-based Authentication0
Free Lunch for Federated Remote Sensing Target Fine-Grained Classification: A Parameter-Efficient Framework0
Free Privacy Protection for Wireless Federated Learning: Enjoy It or Suffer from It?0
Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learning0
Free-Rider Games for Federated Learning with Selfish Clients in NextG Wireless Networks0
Free-riders in Federated Learning: Attacks and Defenses0
FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning0
Frequency Modulation Aggregation for Federated Learning0
FRESCO: Federated Reinforcement Energy System for Cooperative Optimization0
FRIDA: Free-Rider Detection using Privacy Attacks0
Combating Client Dropout in Federated Learning via Friend Model Substitution0
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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