  Blockchain has widely been adopted to design accountable federated learning
frameworks; however, the existing frameworks do not scale for distributed model
training over multiple independent blockchain networks. For storing the
pre-trained models over blockchain, current approaches primarily embed a model
using its structural properties that are neither scalable for cross-chain
exchange nor suitable for cross-chain verification. This paper proposes an
architectural framework for cross-chain verifiable model training using
federated learning, called Proof of Federated Training (PoFT), the first of its
kind that enables a federated training procedure span across the clients over
multiple blockchain networks. Instead of structural embedding, PoFT uses model
parameters to embed the model over a blockchain and then applies a verifiable
model exchange between two blockchain networks for cross-network model
training. We implement and test PoFT over a large-scale setup using Amazon EC2
instances and observe that cross-chain training can significantly boosts up the
model efficacy. In contrast, PoFT incurs marginal overhead for inter-chain
model exchanges.

Distributed, Parallel, and Cluster Computing;Systems and Control;Systems and Control