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Distributed Optimization
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The goal of Distributed Optimization is to optimize a certain objective defined over millions of billions of data that is distributed over many machines by utilizing the computational power of these machines.
Source: Analysis of Distributed StochasticDual Coordinate Ascent
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/shuhuayu/dist-sign-momentum
SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum
https://github.com/thejungwon/gc-fed
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
https://github.com/tum-vision/povar
https://github.com/vaseline555/Federated-Learning-in-PyTorch
https://github.com/woodenchild95/fl-simulator
https://github.com/yjlee22/FedShare
https://github.com/zhuangdizhu/FedGen
https://github.com/zshuai8/FedGMM_ICML2023
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.