Scaffold: Stochastic Controlled Averaging for Federated Learning

doi:doi:10.57702/k2c5jpf2

Scaffold: Stochastic Controlled Averaging for Federated Learning

Federated learning has emerged as an important paradigm in modern large-scale machine learning. Unlike in traditional centralized learning where models are trained using large datasets stored in a central server, in federated learning, the training data remains distributed over a large number of clients, which may be phones, network sensors, hospitals, or alternative local information sources.

BibTex:

@dataset{Sai_Praneeth_Karimireddy_and_Satyen_Kale_and_Mehryar_Mohri_and_Sashank_J_Reddi_and_Sebastian_U_Stich_and_Ananda_Theertha_Suresh_2024,
    abstract = {Federated learning has emerged as an important paradigm in modern large-scale machine learning. Unlike in traditional centralized learning where models are trained using large datasets stored in a central server, in federated learning, the training data remains distributed over a large number of clients, which may be phones, network sensors, hospitals, or alternative local information sources.},
    author = {Sai Praneeth Karimireddy and Satyen Kale and Mehryar Mohri and Sashank J. Reddi and Sebastian U. Stich and Ananda Theertha Suresh},
    doi = {10.57702/k2c5jpf2},
    institution = {No Organization},
    keyword = {'client-drift', 'distributed optimization', 'federated learning', 'heterogeneous data'},
    month = {dec},
    publisher = {TIB},
    title = {Scaffold: Stochastic Controlled Averaging for Federated Learning},
    url = {https://service.tib.eu/ldmservice/dataset/scaffold--stochastic-controlled-averaging-for-federated-learning},
    year = {2024}
}