  Federated learning (FL) is an emerging machine learning paradigm that allows
to accomplish model training without aggregating data at a central server. Most
studies on FL consider a centralized framework, in which a single server is
endowed with a central authority to coordinate a number of devices to perform
model training in an iterative manner. Due to stringent communication and
bandwidth constraints, such a centralized framework has limited scalability as
the number of devices grows. To address this issue, in this paper, we propose a
ConFederated Learning (CFL) framework. The proposed CFL consists of multiple
servers, in which each server is connected with an individual set of devices as
in the conventional FL framework, and decentralized collaboration is leveraged
among servers to make full use of the data dispersed throughout the network. We
develop an alternating direction method of multipliers (ADMM) algorithm for
CFL. The proposed algorithm employs a random scheduling policy which randomly
selects a subset of devices to access their respective servers at each
iteration, thus alleviating the need of uploading a huge amount of information
from devices to servers. Theoretical analysis is presented to justify the
proposed method. Numerical results show that the proposed method can converge
to a decent solution significantly faster than gradient-based FL algorithms,
thus boasting a substantial advantage in terms of communication efficiency.
