  Federated learning (FL) enables collaborative training of a shared model on
edge devices while maintaining data privacy. FL is effective when dealing with
independent and identically distributed (iid) datasets, but struggles with
non-iid datasets. Various personalized approaches have been proposed, but such
approaches fail to handle underlying shifts in data distribution, such as data
distribution skew commonly observed in real-world scenarios (e.g., driver
behavior in smart transportation systems changing across time and location).
Additionally, trust concerns among unacquainted devices and security concerns
with the centralized aggregator pose additional challenges. To address these
challenges, this paper presents a dynamically optimized personal deep learning
scheme based on blockchain and federated learning. Specifically, the innovative
smart contract implemented in the blockchain allows distributed edge devices to
reach a consensus on the optimal weights of personalized models. Experimental
evaluations using multiple models and real-world datasets demonstrate that the
proposed scheme achieves higher accuracy and faster convergence compared to
traditional federated and personalized learning approaches.
