https://github.com/wenkehuang/marsfl
공식 코드Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark
Papers with Code 아카이브
Federated Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 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/wenlixiao-cs/wirelessfl
공식 코드Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks
https://github.com/wenzhifang/mtgc
공식 코드Hierarchical Federated Learning with Multi-Timescale Gradient Correction
https://github.com/wfwf10/feature-based-federated-transfer-learning
공식 코드Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer Learning
https://github.com/wfwf10/mkor
공식 코드Maximum Knowledge Orthogonality Reconstruction with Gradients in Federated Learning
https://github.com/whopriyam/Benchmarking-Differential-Privacy-and-Federated-Learning-for-BERT-Models
공식 코드Benchmarking Differential Privacy and Federated Learning for BERT Models
https://github.com/whu-totemdb/FedAPM
공식 코드FedAPM: Federated Learning via ADMM with Partial Model Personalization
https://github.com/williamyi96/scafflix
공식 코드Explicit Personalization and Local Training: Double Communication Acceleration in Federated Learning
https://github.com/wizard1203/fusefl
공식 코드FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion
https://github.com/wizard1203/vhl
공식 코드Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning
https://github.com/wmabebe/lefl
공식 코드https://github.com/wnma3mz/fedlmd
공식 코드https://github.com/wnn2000/fedaaai
공식 코드https://github.com/wnn2000/fediic
공식 코드FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image Classification
https://github.com/wnn2000/fednoro
공식 코드https://github.com/wnn2000/ffl4mia
공식 코드https://github.com/wns823/medical_federated
공식 코드Towards the Practical Utility of Federated Learning in the Medical Domain
https://github.com/wonder7racer/s2fgl
공식 코드https://github.com/woodenchild95/fl-simulator
공식 코드FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy
https://github.com/wph6/fhbench
공식 코드FHBench: Towards Efficient and Personalized Federated Learning for Multimodal Healthcare
https://github.com/wrh14/learning_to_invert
공식 코드Learning to Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning
https://github.com/wsqwsq/Towards-a-Defense-against-Backdoor-Attacks-in-Continual-Federated-Learning
공식 코드Towards a Defense Against Federated Backdoor Attacks Under Continuous Training
https://github.com/wtimc/Privacy-Amplification-for-Federated-Learning-via-User-Sampling-and-Wireless-Aggregation
공식 코드Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregation
https://github.com/wuzhiyuan2000/FedAgg
공식 코드Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration
https://github.com/wuzhiyuan2000/fedcache
공식 코드FedCache 2.0: Federated Edge Learning with Knowledge Caching and Dataset Distillation
https://github.com/wwh0411/fedmabench
공식 코드FedMABench: Benchmarking Mobile Agents on Decentralized Heterogeneous User Data
https://github.com/wwh0411/fedrsu
공식 코드FedRSU: Federated Learning for Scene Flow Estimation on Roadside Units
https://github.com/wwh0411/knowledgesg
공식 코드KnowledgeSG: Privacy-Preserving Synthetic Text Generation with Knowledge Distillation from Server
https://github.com/wwzzz/fedgs
공식 코드FedGS: Federated Graph-based Sampling with Arbitrary Client Availability
https://github.com/wyjeong/FedMatch
공식 코드Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning
https://github.com/wyjeong/FedWeIT
공식 코드Federated Continual Learning with Weighted Inter-client Transfer
https://github.com/wzjialang/pfedsis
공식 코드Personalizing Federated Instrument Segmentation with Visual Trait Priors in Robotic Surgery
https://github.com/xbfu/fedspray
공식 코드https://github.com/xcgoner/async_fl
공식 코드https://github.com/xenialll/foogd-main
공식 코드FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detection
https://github.com/xiaoni-61/dh-benchmark
공식 코드Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.