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Federated Learning

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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/zhxchd/sdar_splitnn

공식 코드

Passive Inference Attacks on Split Learning via Adversarial Regularization

https://github.com/zhyczy/fed-co2

공식 코드

Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning

https://github.com/zhyczy/fedtp

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FedTP: Federated Learning by Transformer Personalization

https://github.com/zhzhang2018/decentralizedfl

공식 코드

The Effect of Training Parameters and Mechanisms on Decentralized Federated Learning based on MNIST Dataset

https://github.com/zi-yuanyang/hyperfed

공식 코드

Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT Imaging

https://github.com/zi-yuanyang/psfed-palm

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Physics-Driven Spectrum-Consistent Federated Learning for Palmprint Verification

https://github.com/zi-yuanyang/ros-fl

공식 코드

Robust Split Federated Learning for U-shaped Medical Image Networks

https://github.com/zib-iol/feddistill

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On the Byzantine-Resilience of Distillation-Based Federated Learning

https://github.com/zibinpan/FedOSD

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Federated Unlearning with Gradient Descent and Conflict Mitigation

https://github.com/zihangxiang/-practical-differentially-private-and-byzantine-resilient-federated

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Practical Differentially Private and Byzantine-resilient Federated Learning

https://github.com/zijianzhang/CARLA_INVS

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Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehicles

https://github.com/ziruniu0/fedspu

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FedSPU: Personalized Federated Learning for Resource-constrained Devices with Stochastic Parameter Update

https://github.com/ziruniu0/flrce

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FLrce: Resource-Efficient Federated Learning with Early-Stopping Strategy

https://github.com/zj-jayzhang/DENSE

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DENSE: Data-Free One-Shot Federated Learning

https://github.com/zj-jayzhang/Federated-Class-Continual-Learning

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TARGET: Federated Class-Continual Learning via Exemplar-Free Distillation

https://github.com/zju-llms/awesome-loras

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A Survey on LoRA of Large Language Models

https://github.com/zjysteven/privayattack_at_fl

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Privacy Leakage of Adversarial Training Models in Federated Learning Systems

https://github.com/zk23du/fedli

공식 코드

Towards Hyper-parameter-free Federated Learning

https://github.com/zkhku/fedsage

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Subgraph Federated Learning with Missing Neighbor Generation

https://github.com/zlijingtao/ResSFL

공식 코드

ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning

https://github.com/zm17943/SplitAVG

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SplitAVG: A heterogeneity-aware federated deep learning method for medical imaging

https://github.com/zoesgithub/fedreg

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Acceleration of Federated Learning with Alleviated Forgetting in Local Training

https://github.com/zpfhnu/artiststyle18

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FedStyle: Style-Based Federated Learning Crowdsourcing Framework for Art Commissions

https://github.com/zshuai8/FedGMM_ICML2023

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Personalized Federated Learning under Mixture of Distributions

https://github.com/zsk66/flame

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On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and Fairness

https://github.com/zxlovely/FedKF

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Handling Data Heterogeneity in Federated Learning via Knowledge Distillation and Fusion

https://github.com/zyc140345/fedamole

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Personalized Federated Fine-Tuning for LLMs via Data-Driven Heterogeneous Model Architectures

https://github.com/zywang0701/drl-simus

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Distributionally Robust Learning for Multi-source Unsupervised Domain Adaptation

https://gitlab.com/compromise3/co-defend

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CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat Detection

https://gitlab.com/compromise3/desrutge

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Realistic Urban Traffic Generator using Decentralized Federated Learning for the SUMO simulator

https://gitlab.com/eijikawasaki/double-pendulum

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Data Subsampling for Bayesian Neural Networks

https://gitlab.com/maria.hartmann/FedPref

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FedPref: Federated Learning Across Heterogeneous Multi-objective Preferences

https://gitlab.lrz.de/hctl/TurboSVM-FL

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TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy Clients

https://github.com/1hb6s7t/Data

Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Data

https://github.com/564612540/FedPD

FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data

https://github.com/564612540/Hybrid-Federated-Learning

Hybrid Federated Learning: Algorithms and Implementation

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.