Stochastic Coded Federated Learning with Convergence and Privacy Guarantees. (arXiv:2201.10092v1 [cs.LG])

Federated learning (FL) has attracted much attention as a privacy-preserving
distributed machine learning framework, where many clients collaboratively
train a machine learning model by exchanging model updates with a parameter
server instead of sharing their raw data. Nevertheless, FL training suffers
from slow convergence and unstable performance due to stragglers caused by the
heterogeneous computational resources of clients and fluctuating communication
rates. This paper proposes a coded FL framework, namely *stochastic coded
federated learning* (SCFL) to mitigate the straggler issue. …

arxiv federated learning learning privacy stochastic