  Decentralized learning (DL) has gained prominence for its potential benefits
in terms of scalability, privacy, and fault tolerance. It consists of many
nodes that coordinate without a central server and exchange millions of
parameters in the inherently iterative process of machine learning (ML)
training. In addition, these nodes are connected in complex and potentially
dynamic topologies. Assessing the intricate dynamics of such networks is
clearly not an easy task. Often in literature, researchers resort to simulated
environments that do not scale and fail to capture practical and crucial
behaviors, including the ones associated to parallelism, data transfer, network
delays, and wall-clock time. In this paper, we propose DecentralizePy, a
distributed framework for decentralized ML, which allows for the emulation of
large-scale learning networks in arbitrary topologies. We demonstrate the
capabilities of DecentralizePy by deploying techniques such as sparsification
and secure aggregation on top of several topologies, including dynamic networks
with more than one thousand nodes.
