  In this paper, we present a study of a Federated Learning (FL) system, based
on the use of decentralized architectures to ensure trust and increase
reliability. The system is based on the idea that the FL collaborators upload
the (ciphered) model parameters on the Inter-Planetary File System (IPFS) and
interact with a dedicated smart contract to track their behavior. Thank to this
smart contract, the phases of parameter updates are managed efficiently,
thereby strengthening data security. We have carried out an experimental study
that exploits two different methods of weight aggregation, i.e., a classic
averaging scheme and a federated proximal aggregation. The results confirm the
feasibility of the proposal.
