LEGO-FL: Learning Heterogeneous Federated Models as a LEGO Assembly Games

Just as LEGO pieces can be assembled into an unlimited variety of structures, heterogeneous federated learning (HFL) can be viewed as the assembly of diverse model components. Inspired by this analogy, we reformulate HFL as a LEGO-like assembly game. The central challenge in HFL lies in learning across heterogeneous model architectures, which hinders direct parameter sharing. To address this challenge, we propose to decompose models into a set of standardized, modular components—analogous to LEGO pieces, and then to learn these components collaboratively across clients. We refer to these components as model blocks. This paper investigates how to learn and assemble them under predefined composition rules to construct heterogeneous models. Based on this perspective, we develop a novel federated learning framework, termed LEGO-FL, which enables flexible model construction while preserving collaborative learning. We evaluate the proposed method through small-scale experimental studies and demonstrate its feasibility. Finally, we discuss potential extensions of LEGO-FL to large-scale federated settings and more complex model architectures.