https://github.com/robertomatheuspp/clustering_ee_fl
공식 코드Energy-Efficient Federated Learning for AIoT using Clustering Methods
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/romainchor/datascience
공식 코드Rate-Distortion Theoretic Bounds on Generalization Error for Distributed Learning
https://github.com/romainchor/generalization_fl_icml2024
공식 코드Lessons from Generalization Error Analysis of Federated Learning: You May Communicate Less Often!
https://github.com/rong-dai/co-boosting
공식 코드Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting
https://github.com/rong-dai/dispfl
공식 코드https://github.com/royson/reefl
공식 코드Recurrent Early Exits for Federated Learning with Heterogeneous Clients
https://github.com/rruisong/fedbevt
공식 코드FedBEVT: Federated Learning Bird's Eye View Perception Transformer in Road Traffic Systems
https://github.com/rruisong/fedd3
공식 코드Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments
https://github.com/rruisong/h2-fed
공식 코드Federated Learning Framework Coping with Hierarchical Heterogeneity in Cooperative ITS
https://github.com/rub-ksv/federated-learning-asr
공식 코드https://github.com/rui-yan/ssl-fl
공식 코드https://github.com/rui-ye/fedllm-bench
공식 코드FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models
https://github.com/rui-ye/openfedllm
공식 코드OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning
https://github.com/ruohliuq/fedmeta
공식 코드Learning Task-Aware Energy Disaggregation: a Federated Approach
https://github.com/russelljeffrey/FedBrain-Distill
공식 코드https://github.com/russelljeffrey/TPFL
공식 코드TPFL: Tsetlin-Personalized Federated Learning with Confidence-Based Clustering
https://github.com/s-huu/pmtl
공식 코드Private Multi-Task Learning: Formulation and Applications to Federated Learning
https://github.com/s222416822/bqfl
공식 코드Decentralized Quantum Federated Learning for Metaverse: Analysis, Design and Implementation
https://github.com/s222416822/llmqfl
공식 코드LLM-QFL: Distilling Large Language Model for Quantum Federated Learning
https://github.com/s222416822/pqc-qfl-model
공식 코드https://github.com/sacs-epfl/fens
공식 코드https://github.com/sadafsk/fl_rd
공식 코드Lossy Gradient Compression: How Much Accuracy Can One Bit Buy?
https://github.com/sadangelf/fedrf-tca
공식 코드Robust and Communication-Efficient Federated Domain Adaptation via Random Features
https://github.com/saeedhaghighi/vanet
공식 코드Hierarchical Federated Learning in Multi-hop Cluster-Based VANETs
https://github.com/sagnik106/clustered-fl-ga
공식 코드Genetic CFL: Optimization of Hyper-Parameters in Clustered Federated Learning
https://github.com/sahelyiyi/FederatedLearning
공식 코드https://github.com/sajjad-ucsb/pfedmma
공식 코드pFedMMA: Personalized Federated Fine-Tuning with Multi-Modal Adapter for Vision-Language Models
https://github.com/sakshi09ch/codec
공식 코드CoDeC: Communication-Efficient Decentralized Continual Learning
https://github.com/salierilu/fl_fault_diagnosis
공식 코드Federated Learning with Uncertainty-Based Client Clustering for Fleet-Wide Fault Diagnosis
https://github.com/salmakh1/filfl
공식 코드FilFL: Client Filtering for Optimized Client Participation in Federated Learning
https://github.com/samuelhorvath/vr_byzantine
공식 코드https://github.com/sanjana-sarda/energy_prediction
공식 코드https://github.com/sanoscience/cxr-fl
공식 코드CXR-FL: Deep Learning-Based Chest X-ray Image Analysis Using Federated Learning
https://github.com/sanphouwang/fedccrl
공식 코드FedCCRL: Federated Domain Generalization with Cross-Client Representation Learning
https://github.com/saptarshic27/fl
공식 코드A Statistical Analysis of Deep Federated Learning for Intrinsically Low-dimensional Data
https://github.com/sarababakn/flash_fl
공식 코드Lottery Aware Sparsity Hunting: Enabling Federated Learning on Resource-Limited Edge
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.