  Federated Learning (FL) presents a decentralized approach to model training
in the agri-food sector and offers the potential for improved machine learning
performance, while ensuring the safety and privacy of individual farms or data
silos. However, the conventional FL approach has two major limitations. First,
the heterogeneous data on individual silos can cause the global model to
perform well for some clients but not all, as the update direction on some
clients may hinder others after they are aggregated. Second, it is lacking with
respect to the efficiency perspective concerning communication costs during FL
and large model sizes. This paper proposes a new technical solution that
utilizes network pruning on client models and aggregates the pruned models.
This method enables local models to be tailored to their respective data
distribution and mitigate the data heterogeneity present in agri-food data.
Moreover, it allows for more compact models that consume less data during
transmission. We experiment with a soybean yield forecasting dataset and find
that this approach can improve inference performance by 15.5% to 20% compared
to FedAvg, while reducing local model sizes by up to 84% and the data volume
communicated between the clients and the server by 57.1% to 64.7%.
