Heterogeneous Federated Learning
This work addresses the challenge of applying federated learning to heterogeneous settings, which is an incremental improvement for distributed machine learning systems.
The paper tackles the problem of structural misalignment in federated learning due to heterogeneous data by proposing a framework that aligns model structures with feature information, resulting in improved convergence speed, accuracy, and efficiency in both IID and non-IID scenarios.
Federated learning learns from scattered data by fusing collaborative models from local nodes. However, due to chaotic information distribution, the model fusion may suffer from structural misalignment with regard to unmatched parameters. In this work, we propose a novel federated learning framework to resolve this issue by establishing a firm structure-information alignment across collaborative models. Specifically, we design a feature-oriented regulation method ({$Ψ$-Net}) to ensure explicit feature information allocation in different neural network structures. Applying this regulating method to collaborative models, matchable structures with similar feature information can be initialized at the very early training stage. During the federated learning process under either IID or non-IID scenarios, dedicated collaboration schemes further guarantee ordered information distribution with definite structure matching, so as the comprehensive model alignment. Eventually, this framework effectively enhances the federated learning applicability to extensive heterogeneous settings, while providing excellent convergence speed, accuracy, and computation/communication efficiency.