  The proliferation of resourceful mobile devices that store rich,
multidimensional and privacy-sensitive user data motivate the design of
federated learning (FL), a machine-learning (ML) paradigm that enables mobile
devices to produce an ML model without sharing their data. However, the
majority of the existing FL frameworks rely on centralized entities. In this
work, we introduce IPLS, a fully decentralized federated learning framework
that is partially based on the interplanetary file system (IPFS). By using IPLS
and connecting into the corresponding private IPFS network, any party can
initiate the training process of an ML model or join an ongoing training
process that has already been started by another party. IPLS scales with the
number of participants, is robust against intermittent connectivity and dynamic
participant departures/arrivals, requires minimal resources, and guarantees
that the accuracy of the trained model quickly converges to that of a
centralized FL framework with an accuracy drop of less than one per thousand.
