Table 1.

Distributed learning vs. federated learning.

	Distributed Learning	Federated Learning
Aims	Scalable parallelized big data processing	Processing distributed data on heterogeneous data sources
Datasets	IID (identically and independently distributed) and have roughly the same size	Heterogeneous data sets; their sizes may span several orders of magnitude
Nodes	Data centers with powerful computing resources connected by high-performance networks	Often low-power devices, such as smartphones and IoT devices, with limited computing resources connected by less reliable networks, which results in their failures or dropping out of computation