  In this paper, we study a new latency optimization problem for
blockchain-based federated learning (BFL) in multi-server edge computing. In
this system model, distributed mobile devices (MDs) communicate with a set of
edge servers (ESs) to handle both machine learning (ML) model training and
block mining simultaneously. To assist the ML model training for
resource-constrained MDs, we develop an offloading strategy that enables MDs to
transmit their data to one of the associated ESs. We then propose a new
decentralized ML model aggregation solution at the edge layer based on a
consensus mechanism to build a global ML model via peer-to-peer (P2P)-based
blockchain communications. Blockchain builds trust among MDs and ESs to
facilitate reliable ML model sharing and cooperative consensus formation, and
enables rapid elimination of manipulated models caused by poisoning attacks. We
formulate latency-aware BFL as an optimization aiming to minimize the system
latency via joint consideration of the data offloading decisions, MDs' transmit
power, channel bandwidth allocation for MDs' data offloading, MDs'
computational allocation, and hash power allocation. Given the mixed action
space of discrete offloading and continuous allocation variables, we propose a
novel deep reinforcement learning scheme with a parameterized advantage actor
critic algorithm. We theoretically characterize the convergence properties of
BFL in terms of the aggregation delay, mini-batch size, and number of P2P
communication rounds. Our numerical evaluation demonstrates the superiority of
our proposed scheme over baselines in terms of model training efficiency,
convergence rate, system latency, and robustness against model poisoning
attacks.
