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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/zehuilu/Cooperative-Tuning-of-Multi-Agent-Optimal-Control-Systems
공식 코드https://github.com/zoesgithub/fedreg
공식 코드Acceleration of Federated Learning with Alleviated Forgetting in Local Training
https://github.com/AshwinRJ/Federated-Learning-PyTorch
Federated Learning: Challenges, Methods, and Future Directions
https://github.com/BRAIN-chain/FRAIN
Federated Learning: Challenges, Methods, and Future Directions
https://github.com/Capsar/dat
Distributed Adversarial Training to Robustify Deep Neural Networks at Scale
https://github.com/DAve-QN/source
Communication Efficient Distributed Optimization using an Approximate Newton-type Method
https://github.com/DavidsonAle/Robust-Learning---Effects-of-Mutual-Information-Penalization
https://github.com/KarhouTam/FL-bench
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
https://github.com/KarhouTam/SCAFFOLD-PyTorch
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
https://github.com/Koukyosyumei/AIJack
https://github.com/MLOPTPSU/TorchFed
https://github.com/MediaBrain-SJTU/FedLESAM
https://github.com/PaddlePaddle/FleetX/blob/develop/examples/resnet/train_fleet_static_localsgd.py
Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronization
https://github.com/TezRomacH/layer-to-layer-pytorch
Training Large Neural Networks with Constant Memory using a New Execution Algorithm
https://github.com/TsingZ0/PFL-Non-IID
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
https://github.com/adelnabli/dadao
Optimal algorithms for smooth and strongly convex distributed optimization in networks
https://github.com/anathan90/RADiSA
Optimization for Large-Scale Machine Learning with Distributed Features and Observations
https://github.com/bdemo/pfedbred_public
https://github.com/carbonati/fl-zoo
https://github.com/epfml/cola
CoCoA: A General Framework for Communication-Efficient Distributed Optimization
https://github.com/facebookresearch/fairscale
Training Large Neural Networks with Constant Memory using a New Execution Algorithm
https://github.com/gaoliang13/feddc
https://github.com/google-research/public-data-in-dpfl
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
https://github.com/hua-zi/FedCFA
FedCFA: Alleviating Simpson's Paradox in Model Aggregation with Counterfactual Federated Learning
https://github.com/intel/openfl
https://github.com/jaemin-shin/fedbalancer
https://github.com/ki-ljl/Scaffold-Federated-Learning
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
https://github.com/learning-at-home/hivemind
Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices
https://github.com/maxencenoble/Differential-Privacy-for-Heterogeneous-Federated-Learning
https://github.com/mohamad-m2/federated-learning
https://github.com/morningD/GrouProx
https://github.com/nliulab/fl-benchmark
https://github.com/omarfoq/fedem
https://github.com/omarfoq/knn-per
https://github.com/ramshi236/Accelerated-Federated-Learning-Over-MAC-in-Heterogeneous-Networks
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.