Papers with Code 아카이브
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/judydnguyen/pardon-feddg
공식 코드PARDON: Privacy-Aware and Robust Federated Domain Generalization
https://github.com/jun-b0518/fedhydra
공식 코드A Unified Solution to Diverse Heterogeneities in One-shot Federated Learning
https://github.com/junlianglv/task_diversity_bfl
공식 코드https://github.com/junyizhu-ai/confidence_aware_pfl
공식 코드Confidence-aware Personalized Federated Learning via Variational Expectation Maximization
https://github.com/junyizhu-ai/randomsparsification
공식 코드Improving Differentially Private SGD via Randomly Sparsified Gradients
https://github.com/junyizhu-ai/surrogate_model_extension
공식 코드Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning
https://github.com/justaguyalways/stochastic-federated-differentially-private-and-fair-learning
공식 코드A Stochastic Optimization Framework for Private and Fair Learning From Decentralized Data
https://github.com/juyongjiang/vfedpca-vfedakpca
공식 코드https://github.com/juzehao/befl
공식 코드BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT
https://github.com/jw9msjwjnpdrlfw/tsfl
공식 코드STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networks
https://github.com/jwicaksana/fca
공식 코드FCA: Taming Long-tailed Federated Medical Image Classification by Classifier Anchoring
https://github.com/jwicaksana/fedmix
공식 코드FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation
https://github.com/jyzgh/dapperfl
공식 코드DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices
https://github.com/jyzgh/fedlps
공식 코드FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter Sharing
https://github.com/k1l1/SLT
공식 코드Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices
https://github.com/k1l1/cocofl
공식 코드https://github.com/kai-yue/fedvote
공식 코드https://github.com/kai-yue/predictive-coding-fl
공식 코드Communication-Efficient Federated Learning via Predictive Coding
https://github.com/kampmichael/feddc
공식 코드https://github.com/kareemgamalmahmoud/federated-learning-based-multilingual-emoji-prediction-in-clean-and-attack-scenarios
공식 코드Federated Learning Based Multilingual Emoji Prediction In Clean and Attack Scenarios
https://github.com/kasraborazjani/dgb-pcw-fl
공식 코드Multi-Modal Federated Learning for Cancer Staging over Non-IID Datasets with Unbalanced Modalities
https://github.com/kasraborazjani/task-perspective-het
공식 코드https://github.com/kazkara/adept
공식 코드ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning
https://github.com/kcl-bmeis/ultraflwr
공식 코드UltraFlwr -- An Efficient Federated Medical and Surgical Object Detection Framework
https://github.com/kclip/DSVGD
공식 코드Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient Descent
https://github.com/kelenlv/learn2pfed
공식 코드https://github.com/kenziyuliu/private-cross-silo-fl
공식 코드On Privacy and Personalization in Cross-Silo Federated Learning
https://github.com/khachoang1412/SecAgg_not_private
공식 코드Secure Aggregation is Not Private Against Membership Inference Attacks
https://github.com/kimwj94/ProtoFSSL
공식 코드Federated Semi-Supervised Learning with Prototypical Networks
https://github.com/kirschte/dphelmet
공식 코드Distributed DP-Helmet: Scalable Differentially Private Non-interactive Averaging of Single Layers
https://github.com/kitsuyaazuma/scarlet
공식 코드Soft-Label Caching and Sharpening for Communication-Efficient Federated Distillation
https://github.com/koalayan/skymask
공식 코드SkyMask: Attack-agnostic Robust Federated Learning with Fine-grained Learnable Masks
https://github.com/kpandl/Scalable-Data-Point-Valuation-in-Decentralized-Learning
공식 코드https://github.com/kpandl/reward-system-for-trustworthy-medical-federated-learning
공식 코드https://github.com/krishnap25/RFA
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.