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Federated Learning
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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/NaiboWang/FedELMY
공식 코드One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversity
https://github.com/Naram-m/Datasets-Models
공식 코드https://github.com/NguyenThaiVu/prune_fl_project
공식 코드Automatic Structured Pruning for Efficient Architecture in Federated Learning
https://github.com/OpenMined/PyDentity/tree/master/projects/aries-fl
공식 코드https://github.com/OpenMined/PyVertical
공식 코드PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN
https://github.com/Optimization-and-Machine-Learning-Lab/energy_fed_trpo
공식 코드Generalizing in Net-Zero Microgrids: A Study with Federated PPO and TRPO
https://github.com/Oxfordblue7/GCFL
공식 코드https://github.com/PanBikang/PromptFolio
공식 코드Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method
https://github.com/PatrickZH/Improved-Deep-Leakage-from-Gradients
공식 코드https://github.com/Pengxin-Guo/FedSA-LoRA
공식 코드Selective Aggregation for Low-Rank Adaptation in Federated Learning
https://github.com/Practicing-Federated-Learning-for-IoT/FedGRU
공식 코드Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach
https://github.com/Pranabiitp/FedDUAL
공식 코드https://github.com/Princeton-SysML/GradAttack
공식 코드Evaluating Gradient Inversion Attacks and Defenses in Federated Learning
https://github.com/QinbinLi/FedKT
공식 코드Practical One-Shot Federated Learning for Cross-Silo Setting
https://github.com/RUB-InfSec/distributed_learning_robustness
공식 코드On the Robustness of Distributed Machine Learning against Transfer Attacks
https://github.com/RaghavSinghal10/fedex-lora
공식 코드FedEx-LoRA: Exact Aggregation for Federated and Efficient Fine-Tuning of Foundation Models
https://github.com/Rand2AI/AdaDefense
공식 코드Gradients Stand-in for Defending Deep Leakage in Federated Learning
https://github.com/Rand2AI/GRNN
공식 코드GRNN: Generative Regression Neural Network -- A Data Leakage Attack for Federated Learning
https://github.com/Raymw/Federated-XGBoost
공식 코드The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost
https://github.com/RenpuLiu/flute
공식 코드Federated Representation Learning in the Under-Parameterized Regime
https://github.com/RickZack/FedSeq
공식 코드Speeding up Heterogeneous Federated Learning with Sequentially Trained Superclients
https://github.com/RickZack/GHBM
공식 코드Communication-Efficient Heterogeneous Federated Learning with Generalized Heavy-Ball Momentum
https://github.com/RingBDStack/Fed_mood
공식 코드FedMood: Federated Learning on Mobile Health Data for Mood Detection
https://github.com/S1mple-yyds/Fed_Epi_Classifier
공식 코드Towards the efficacy of federated prediction for epidemics on networks
https://github.com/SEED-VT/FedDebug
공식 코드FedDebug: Systematic Debugging for Federated Learning Applications
https://github.com/SMILELab-FL/FedLab
공식 코드https://github.com/Saber-mm/HDPFL
공식 코드Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning
https://github.com/SamuelFountain/FLStore
공식 코드FLStore: Efficient Federated Learning Storage for non-training workloads
https://github.com/SamuelHorvath/Compressed_SGD_PyTorch
공식 코드A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning
https://github.com/SamuelHorvath/FL-optimal-client-sampling
공식 코드https://github.com/SciML-FL/gan-filter
공식 코드Robust Federated Learning Against Poisoning Attacks: A GAN-Based Defense Framework
https://github.com/ShahryarBQ/DSpodFL
공식 코드https://github.com/ShahryarBQ/EF_HC
공식 코드Decentralized Event-Triggered Federated Learning with Heterogeneous Communication Thresholds
https://github.com/ShawnXYang/AccumulativeAttack
공식 코드https://github.com/ShenGroup/FMAB
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.