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/krishnap25/simplicial-fl
공식 코드Device Heterogeneity in Federated Learning: A Superquantile Approach
https://github.com/krumpguo/fedvls
공식 코드https://github.com/ksreenivasan/OOD_Federated_Learning
공식 코드Attack of the Tails: Yes, You Really Can Backdoor Federated Learning
https://github.com/kun0906/brifca
공식 코드https://github.com/kun0906/flr
공식 코드Financial Data Analysis with Robust Federated Logistic Regression
https://github.com/kungfuai/flwr_serverless
공식 코드https://github.com/kxzxvbk/Fling
공식 코드https://github.com/labRadioVision/federated
공식 코드Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networks
https://github.com/labsaint/mud-hog_federated_learning
공식 코드https://github.com/lachholden/fednerf-pytorch
공식 코드https://github.com/lancasterJie/FLAYER
공식 코드Optimizing Personalized Federated Learning through Adaptive Layer-Wise Learning
https://github.com/lancopku/fedmnmt
공식 코드Communication Efficient Federated Learning for Multilingual Neural Machine Translation with Adapter
https://github.com/langnatalie/jopeq
공식 코드Joint Privacy Enhancement and Quantization in Federated Learning
https://github.com/langnatalie/salf
공식 코드Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates
https://github.com/lapisrocks/fedselect
공식 코드FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuning
https://github.com/lassino/fedbnn_code
공식 코드https://github.com/layer6ai-labs/ProxyFL
공식 코드Decentralized Federated Learning through Proxy Model Sharing
https://github.com/ldxdu/fedfusion
공식 코드FedFusion: Manifold Driven Federated Learning for Multi-satellite and Multi-modality Fusion
https://github.com/leahcimali/Comparative-Evaluation-of-Clustered-Federated-Learning-Methods
공식 코드Comparative Evaluation of Clustered Federated Learning Methods
https://github.com/lebronlambert/FedLPA_NeurIPS2024
공식 코드FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation
https://github.com/lebyni/PFA
공식 코드PFA: Privacy-preserving Federated Adaptation for Effective Model Personalization
https://github.com/lee-va/ebs-cfl
공식 코드EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning
https://github.com/lemingshen/FedConv
공식 코드FedConv: A Learning-on-Model Paradigm for Heterogeneous Federated Clients
https://github.com/leondada/fedapt
공식 코드Federated Adaptive Prompt Tuning for Multi-Domain Collaborative Learning
https://github.com/leondada/fedra
공식 코드https://github.com/leopold1423/fedmud-icml25
공식 코드The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning
https://github.com/lgrativol/fl_exps
공식 코드Federated learning compression designed for lightweight communications
https://github.com/lgrativol/flocora_eusipco24
공식 코드FLoCoRA: Federated learning compression with low-rank adaptation
https://github.com/liamhebert/FedFormer
공식 코드FedFormer: Contextual Federation with Attention in Reinforcement Learning
https://github.com/liangh68/FedReMa
공식 코드FedReMa: Improving Personalized Federated Learning via Leveraging the Most Relevant Clients
https://github.com/liar-mask/fedmia
공식 코드https://github.com/liboyue/Network-Distributed-Algorithm
공식 코드https://github.com/liehe/prfl
공식 코드https://github.com/lightbulb128/pencil
공식 코드Pencil: Private and Extensible Collaborative Learning without the Non-Colluding Assumption
https://github.com/liizhang/LoGofair
공식 코드LoGoFair: Post-Processing for Local and Global Fairness in Federated Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.