@article{1444772,
      recid = {1444772},
      author = {Jiang, Jiawei, and Cui, Bin, and Zhang, Ce,},
      title = {Distributed machine learning and gradient optimization/},
      publisher = {Springer,},
      address = {Singapore :},
      pages = {1 online resource},
      year = {2022},
      abstract = {This book presents the state of the art in distributed  machine learning algorithms that are based on gradient  optimization methods. In the big data era, large-scale  datasets pose enormous challenges for the existing machine  learning systems. As such, implementing machine learning  algorithms in a distributed environment has become a key  technology, and recent research has shown gradient-based  iterative optimization to be an effective solution.  Focusing on methods that can speed up large-scale gradient  optimization through both algorithm optimizations and  careful system implementations, the book introduces three  essential techniques in designing a gradient optimization  algorithm to train a distributed machine learning model:  parallel strategy, data compression and synchronization  protocol. Written in a tutorial style, it covers a range of  topics, from fundamental knowledge to a number of carefully  designed algorithms and systems of distributed machine  learning. It will appeal to a broad audience in the field  of machine learning, artificial intelligence, big data and  database management.},
      url = {http://library.usi.edu/record/1444772},
      doi = {https://doi.org/10.1007/978-981-16-3420-8},
}