https://github.com/xidongwu/d-auprc
공식 코드Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining
Papers with Code 아카이브
Federated Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 12
- 보존된 평가 기록
- 61
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/xidongwu/federated-minimax-and-conditional-stochastic-optimization
공식 코드Solving a Class of Non-Convex Minimax Optimization in Federated Learning
https://github.com/xiehahha/aaai_ppai23_split_learning_leakage
공식 코드Label Inference Attack against Split Learning under Regression Setting
https://github.com/xinghaowu/feddecomp
공식 코드https://github.com/xiyangl3/robust_dp
공식 코드https://github.com/xj231/featureinference-vfl
공식 코드Feature Inference Attack on Model Predictions in Vertical Federated Learning
https://github.com/xjiajiahao/federated-minimax
공식 코드CDMA: A Practical Cross-Device Federated Learning Algorithm for General Minimax Problems
https://github.com/xmed-lab/fed-mas
공식 코드https://github.com/xmed-lab/rscfed
공식 코드RSCFed: Random Sampling Consensus Federated Semi-supervised Learning
https://github.com/xtra-computing/fedsim
공식 코드A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning
https://github.com/xtra-computing/fet
공식 코드Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data
https://github.com/xtxiatong/flea
공식 코드https://github.com/xu-jingyi/fedcorr
공식 코드FedCorr: Multi-Stage Federated Learning for Label Noise Correction
https://github.com/xu737/PeFAD
공식 코드PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection
https://github.com/xuchenhao001/afl
공식 코드An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportation
https://github.com/xueyu-ubc/fpfc
공식 코드Clustered Federated Learning based on Nonconvex Pairwise Fusion
https://github.com/xuqiongkai/fingerprintattack_on_fl
공식 코드Fingerprint Attack: Client De-Anonymization in Federated Learning
https://github.com/xx987/xfbci
공식 코드Explainable Federated Bayesian Causal Inference and Its Application in Advanced Manufacturing
https://github.com/xztcwang/fedmn
공식 코드Personalized Federated Learning via Heterogeneous Modular Networks
https://github.com/y-xue/radfed
공식 코드https://github.com/yan-gao-GY/Flower-SpeechBrain
공식 코드End-to-End Speech Recognition from Federated Acoustic Models
https://github.com/yanci87/lr-xfl
공식 코드LR-XFL: Logical Reasoning-based Explainable Federated Learning
https://github.com/yandex-research/moshpit-sgd
공식 코드Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices
https://github.com/yangyiliu21/fl_rd
공식 코드M22: A Communication-Efficient Algorithm for Federated Learning Inspired by Rate-Distortion
https://github.com/yankang18/FedCVT
공식 코드FedCVT: Semi-supervised Vertical Federated Learning with Cross-view Training
https://github.com/yankang18/attack-defense-vfl
공식 코드A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated Learning
https://github.com/yankang18/fedcg
공식 코드https://github.com/yaodongyu/tct
공식 코드TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels
https://github.com/yardenas/safe-ef
공식 코드Safe-EF: Error Feedback for Nonsmooth Constrained Optimization
https://github.com/yasar-rehman/L-DAWA
공식 코드https://github.com/ybdai7/Chameleon-durable-backdoor
공식 코드Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning
https://github.com/ybdai7/trojandam-backdoor-defense
공식 코드https://github.com/ybwang119/label_recovery
공식 코드Towards Eliminating Hard Label Constraints in Gradient Inversion Attacks
https://github.com/ycao5602/kafal
공식 코드https://github.com/ycheoo/fppl
공식 코드FPPL: An Efficient and Non-IID Robust Federated Continual Learning Framework
https://github.com/ycheoo/pmae
공식 코드Masked Autoencoders are Parameter-Efficient Federated Continual Learners
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.