arXiv:2312.03120
The Landscape of Modern Machine Learning: A Review of Machine, Distributed and Federated Learning
원문 초록
With the advance of the powerful heterogeneous, parallel and distributed computing systems and ever increasing immense amount of data, machine learning has become an indispensable part of cutting-edge technology, scientific research and consumer products. In this study, we present a review of modern machine and deep learning. We provide a high-level overview for the latest advanced machine learning algorithms, applications, and frameworks. Our discussion encompasses parallel distributed learning, deep learning as well as federated learning. As a result, our work serves as an introductory text to the vast field of modern machine learning.
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Centroid Approximation for Byzantine-Tolerant Federated Learning
FLsim: A Modular and Library-Agnostic Simulation Framework for Federated Learning
Domain Borders Are There to Be Crossed With Federated Few-Shot Adaptation
Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry Detection
Prototype-Guided and Lightweight Adapters for Inherent Interpretation and Generalisation in Federated Learning
FedPall: Prototype-based Adversarial and Collaborative Learning for Federated Learning with Feature Drift
토론
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