https://github.com/matenure/federated_feature_fusion
공식 코드Federated Learning of Models Pre-Trained on Different Features with Consensus Graphs
Papers with Code 아카이브
Federated Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 5
- 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/mattgorb/iterative_parameter_alignment
공식 코드Cross-Silo Federated Learning Across Divergent Domains with Iterative Parameter Alignment
https://github.com/maxencenoble/Differential-Privacy-for-Heterogeneous-Federated-Learning
공식 코드Differentially Private Federated Learning on Heterogeneous Data
https://github.com/maxiaum/kdia
공식 코드https://github.com/maya-simhi/olala
공식 코드OLALa: Online Learned Adaptive Lattice Codes for Heterogeneous Federated Learning
https://github.com/meclabtuda/bottlegan
공식 코드https://github.com/med-air/FedBN
공식 코드FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
https://github.com/med-air/client-dp-fl
공식 코드Client-Level Differential Privacy via Adaptive Intermediary in Federated Medical Imaging
https://github.com/med-air/harmofl
공식 코드HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images
https://github.com/med-air/imfedsemi
공식 코드Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalance
https://github.com/med-air/iop-fl
공식 코드IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentation
https://github.com/mediabrain-sjtu/feddisco
공식 코드FedDisco: Federated Learning with Discrepancy-Aware Collaboration
https://github.com/mediabrain-sjtu/fedgela
공식 코드Federated Learning with Bilateral Curation for Partially Class-Disjoint Data
https://github.com/mediabrain-sjtu/fedmr
공식 코드Federated Learning under Partially Class-Disjoint Data via Manifold Reshaping
https://github.com/mediabrain-sjtu/fedskip
공식 코드FedSkip: Combatting Statistical Heterogeneity with Federated Skip Aggregation
https://github.com/meiyuw/popri
공식 코드Private Federated Learning using Preference-Optimized Synthetic Data
https://github.com/mengcongwo/fl_privacy_blockcrossing
공식 코드Differentially Private Federated Learning via Reconfigurable Intelligent Surface
https://github.com/mengcz13/KDD2021_CNFGNN
공식 코드Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling
https://github.com/mengde-han/vfl-unlearn
공식 코드https://github.com/merce-fra/Reliable-VFL-in-5G-Core-NW-Arch
공식 코드Reliable Vertical Federated Learning in 5G Core Network Architecture
https://github.com/meryemmalakdif/autodfl
공식 코드AutoDFL: A Scalable and Automated Reputation-Aware Decentralized Federated Learning
https://github.com/mhdslh/Federated-Learning-in-Unreliable-and-Resource-Constrained-Cellular-Wireless-Networks
공식 코드Federated Learning in Unreliable and Resource-Constrained Cellular Wireless Networks
https://github.com/microsoft/feddrift
공식 코드https://github.com/microsoft/msrflute
공식 코드FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations
https://github.com/microsoft/personalizedfl
공식 코드Personalized Federated Learning with Adaptive Batchnorm for Healthcare
https://github.com/mikemikezhu/fedilc
공식 코드https://github.com/miketheologitis/FDA-Opt
공식 코드FDA-Opt: Communication-Efficient Federated Fine-Tuning of Language Models
https://github.com/mikudehuane/FedLaAvg
공식 코드Distributed Non-Convex Optimization with Sublinear Speedup under Intermittent Client Availability
https://github.com/mingjiewang0606/fedcl_pubic
공식 코드FedCL: Federated Multi-Phase Curriculum Learning to Synchronously Correlate User Heterogeneity
https://github.com/mingruiliu-ml-lab/communication-efficient-local-gradient-clipping
공식 코드A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks
https://github.com/mingruiliu-ml-lab/episode
공식 코드https://github.com/mingxiang12/fedawe
공식 코드Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability
https://github.com/mingxuts/multi-center-fed-learning
공식 코드Multi-Center Federated Learning: Clients Clustering for Better Personalization
https://github.com/mingzhaoguo/ota-lc
공식 코드Efficient Wireless Federated Learning via Low-Rank Gradient Factorization
https://github.com/minhtcai/MLMF
공식 코드https://github.com/minoskt/PoliBox
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.