  Federated learning (FL), thanks in part to the emergence of the edge
computing paradigm, is expected to enable true real-time applications in
production environments. However, its original dependence on a central server
for orchestration raises several concerns in terms of security, privacy, and
scalability. To solve some of these worries, blockchain technology is expected
to bring decentralization, robustness, and enhanced trust to FL. The
empowerment of FL through blockchain (also referred to as FLchain), however,
has some implications in terms of ledger inconsistencies and age of information
(AoI), which are naturally inherited from the blockchain's fully decentralized
operation. Such issues stem from the fact that, given the temporary ledger
versions in the blockchain, FL devices may use different models for training,
and that, given the asynchronicity of the FL operation, stale local updates
(computed using outdated models) may be generated. In this paper, we shed light
on the implications of the FLchain setting and study the effect that both the
AoI and ledger inconsistencies have on the FL performance. To that end, we
provide a faithful simulation tool that allows capturing the decentralized and
asynchronous nature of the FLchain operation.
