https://github.com/faresmalik/fesvibs
공식 코드FeSViBS: Federated Split Learning of Vision Transformer with Block Sampling
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/fedbiomed/fedbiomed
공식 코드Enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare Applications
https://github.com/fedbkd/fedbkd
공식 코드FedBKD: Distilled Federated Learning to Embrace Gerneralization and Personalization on Non-IID Data
https://github.com/fedcampus/fedcampus_flutter
공식 코드https://github.com/fedcampus/fedkit
공식 코드FedKit: Enabling Cross-Platform Federated Learning for Android and iOS
https://github.com/fedcome/fedcome
공식 코드Federated Learning via Consensus Mechanism on Heterogeneous Data: A New Perspective on Convergence
https://github.com/feddg23/feddg-main
공식 코드https://github.com/fedego/fedego
공식 코드FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphs
https://github.com/fedentropy/fedentropy
공식 코드FedEntropy: Efficient Device Grouping for Federated Learning Using Maximum Entropy Judgment
https://github.com/federallab/openfed
공식 코드OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Framework
https://github.com/federated-learning-experiments/fl-text-models
공식 코드https://github.com/federatedml/fedln
공식 코드Labeling Chaos to Learning Harmony: Federated Learning with Noisy Labels
https://github.com/fedgraph/fedgraph
공식 코드FedGraph: A Research Library and Benchmark for Federated Graph Learning
https://github.com/fedl-repo/fedaux
공식 코드FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learning
https://github.com/fedlearnAI/fedlearn-algo
공식 코드Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform
https://github.com/fedml-ai/fedcv
공식 코드FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks
https://github.com/felixwag/fedunibrain
공식 코드https://github.com/felixwag/staralign
공식 코드https://github.com/fenghz/baffle
공식 코드BAFFLE: A Baseline of Backpropagation-Free Federated Learning
https://github.com/ffhibnese/gifd
공식 코드GIFD: A Generative Gradient Inversion Method with Feature Domain Optimization
https://github.com/fl-har/graph-federated-learning-for-ciot-devices
공식 코드Graph Federated Learning for CIoT Devices in Smart Home Applications
https://github.com/flair-thu/creamfl
공식 코드Multimodal Federated Learning via Contrastive Representation Ensemble
https://github.com/flair-thu/vflair
공식 코드VFLAIR: A Research Library and Benchmark for Vertical Federated Learning
https://github.com/flcao/CoFED
공식 코드https://github.com/flexible-fl/flex-trees
공식 코드https://github.com/flint-xf-fan/Byzantine-Federeated-RL
공식 코드Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee
https://github.com/francescodiana99/fedkit-learn
공식 코드https://github.com/francescopase/federated-klms
공식 코드Adaptive Compression in Federated Learning via Side Information
https://github.com/freddyAdjh/Federated-security
공식 코드Efficient Federated Intrusion Detection in 5G ecosystem using optimized BERT-based model
https://github.com/fudanvi/maecho
공식 코드https://github.com/fumiyukikato/fl-tee
공식 코드https://github.com/fumiyukikato/uldp-fl
공식 코드ULDP-FL: Federated Learning with Across Silo User-Level Differential Privacy
https://github.com/fushuhao6/Attack-Resistant-Federated-Learning
공식 코드Attack-Resistant Federated Learning with Residual-based Reweighting
https://github.com/fwilhelmi/blockchain_enabled_federated_learning
공식 코드Analysis and Evaluation of Synchronous and Asynchronous FLchain
https://github.com/g-pichler/dishonest_mia
공식 코드Perfectly Accurate Membership Inference by a Dishonest Central Server in Federated Learning
https://github.com/g2lab/pcfbl
공식 코드Privacy-preserving patient clustering for personalized federated learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.