FedJigsaw: Multi-Agent Collaborative Model Reassembly for Decentralized Heterogeneous Federated Learning

Model-Heterogeneous Federated Learning (MHFL) enables clients with diverse resource constraints to collaboratively train models with heterogeneous architectures. Existing approaches typically construct client models by extracting sub-models from a predefined global backbone. However, this top-down paradigm confines personalization to a prescribed architectural space. Meanwhile, other architecture-search-based methods impose significant computational and memory overhead to the server. We present FedJigsaw, a novel framework that reformulates model personalization as bottom-up model reassembly. Our key insight is that cohesive functional modules can serve as reusable units of both model structure and learned knowledge. Analogous to assembling a Jigsaw puzzle, each client dynamically constructs its personalized architecture by selecting compatible modules learned locally or obtained from its neighbors. We formulate the assembly process as a sequential decision problem, enabling each client to adapt its architecture to its resource constraints and learning context. To keep this process lightweight, FedJigsaw employs an attention-based pointer network to directly score a variable set of feasible modules, rather than enumerating the complete candidate architectures. Our experimental results demonstrate that, compared with the state-of-the-art MHFL schemes, FedJigsaw improves local accuracy by up to 8.56%, reduces per-client communication by 63.8%, and shortens round time by 17.3%, demonstrating the effectiveness and scalability of module-level reassembly.